Immigration, Work and New Technologies: Access to the Labour Market & Workforce Dynamics (webinar recording)

In this November 2025 P2P conference session, presenters discussed how the labour market is undergoing multifaceted transformations driven by processes of digitalization, with profound implications for the professional trajectories of immigrants. From the recruitment and selection of new workers to employment, entrepreneurship, and employability services, these emerging dynamics exert a significant influence on the ways in which immigrant careers unfold. Technological innovations, together with the normative frameworks they embed within the world of work, prompt critical reflection on digital equity, potential forms of surveillance and control, as well as the opportunities they may create.

The first presentation focuses on a project mapping the digital ecosystem supporting immigrant professionals' employment integration in Canada, specifically exploring the potential of AI and non-AI tools to overcome barriers like credential recognition and underemployment. The second presentation analyzes how digital platforms and Canadian immigration policies shape the precarious labour trajectories of immigrant workers across different urban and national scales, particularly in the gig and tech economies. Finally, the third and fourth presentations examine specific sectors: trucking, focusing on the misclassification of migrant drivers and the impact of technology on surveillance and working conditions, and cybersecurity, exploring how race, gender, and immigration status influence career experiences and contribute to the construction of a "racialized ideal worker." Collectively, the sources address the multi-faceted transformation of the labour market due to digitalization and its profound, often unequal, implications for migrant careers in Canada.

AI-generated transcript:

hello everybody welcome to this session uh my name is Mihi P i have the privilege of being the chair and the moderator of this panel part two of uh the sessions on immigration work and new technologies um I am pleased to uh be joined by researchers from across the country um just as a reminder uh this panel is exploring how the labor market is undergoing multiaceted transformation driven by processes of digitalization with profound implication for the professional trajectory of migrants and we look at how uh from recruitment and selection of new workers to how they access employment their uh decision to move into entrepreneurship and their use of employment services uh the uh dynamics uh generated by emerging and established technology in exert an significant influence on the ways in which migrant careers unfold so we have researchers from uh both the settlement sector as well as different discipline from uh geography political science development studies critical uh legal studies uh occupational therapy and I'm sure I'm missing everyone from this list but uh I'll um I'm sure they will be able to introduce uh their uh their their discipline and their perspective uh very uh very much much better than me i'm sorry uh and this panel is co-organized by Maran Bl who should do a little hello just to sure who presented in the first session myself Milen Kar and Emil Bari uh we are all working from the uh institute for research and migration and society at Concordia University and this panel is part is part of is supported as well by the bridging divides uh seaf project um and so what I'll do because we lacked a little bit of time in the first session I won't go over the name of everyone to then repeat them so I'll instead propose that the first presentation uh by Dr anushia Kasan and her colleague um go uh so that we just go with this first presentation and as we did in the previous panel um you can write in the chat any questions or comments you might have as a presentation proceeds but we'll do a question period uh for all the panelist uh later on so uh Dr kasan may I uh introduce may I invite you to share your screen and start your presentation thank you okay ready thank you for that introduction m bour everyone um thank you for being here um before we start I just wanted to take a moment to acknowledge um the multiple lands that are holding us today um for those of you in person in Halifax um Fami and I are in Calgary on Treaty 7 territory and I also work at the University of British Columbia which is um located on Muscuiam territory today we're going to talk to you about our project entitled mapping the digital ecosystem advanced technology supporting immigrant professionals employment integration in Canada i'm going to start by introducing our research team really briefly and talking a little bit our about our funer i'll then provide some context um for our project and some of the challenges that led us to conduct this study on um the use of AI and nonAI technologies to support professional immigrants in Canada um and I'll then pass it along to my colleague Fami um who will talk to you about our project our project in more specificity including the research question the research design and some of our preliminary results um so as M mentioned my name is Anushia i'm the lead on this project but we have a collaborative team um I believe Katarina and Kisha are in attendance in person um and I'll be presenting with Fami today um who's an adjunct professor at UBC and also a senior researcher at the immigration education society our project is a collaboration between UBC and ties and it has been funded as mentioned before um by the bridging the divides program um which is funded by Canada Canada first research excellent fund and it's meant to explore advanced digital technologies um as was mentioned in the introduction there are people from multiple universities here and so we're um coming from UBC and received the support of the um center for migration studies at UBC so I will jump in and outline some of the challenges um in an audience like this i'm sure that this is common knowledge but I'll just give you a quick snapshot of the literature that we've been grounded in um that looks at you know how Canada continues to be um a a an attractive um space for professional immigrants um but at the same time we know that those folks face an underutilization of their degrees and their skills and also um face deskkilling and often take positions below their capacity um we know that there are tons of barriers with credentials being recognized and licensing becoming um a challenge for folks and folks often um returning to school to obtain a Canadian degree just to facilitate that process um we know that um educated folks have an easier time um resettling in Canada but yet again the their background is not always um honored in addition you know um all kinds of isms systemic discrimination racial bias etc create um more barriers to employment um and we hear from a lot of our participants and I'm sure folks are aware of this employers are looking for Canadian experience and so there's kind of a chicken or an egg happening where folks can't gain Canadian experience because they don't have it and so on and so forth um so we were really interested in looking at how we can support professional immigrants um in having in facing less barriers um less skill discounting less unemployment less underemployment um and increasing their job satisfaction i have a couple of stats that I'll review with you briefly here um that this graph it's a little bit fuzzy hopefully um not so much on your end but really what we see here in the orange line is greater rates of unemployment against new towards newcomers who have been in the country for 5 years or less um and more overeducation and even more so for folks who have been in the country for 10 years or less which is the top line on the right graph um and then on the next slide there are quite a few numbers but I'll just kind of point you to the bottom lines here which show um high level of university degrees obtained by Canadianborn and um recent newcomers i think it's also important to consider that um of those Canadianb borns many of those people would be second generation newcomers like myself where a lot of focus is placed on higher education so with this said I will pass it on to Fami who will jump into um how the digital component of the work um could hopefully help us improve the situation for professional newcomers oh we can't hear you okay uh thank you so much Anusha here I want to focus on settlement sector or service provider organizations i call it SPOS which are the organizations that receive funding from IRCC to support immigrants before 2020 most of the services in the sector were delivered in person and COVID 19 brought a major shift by late 2020 97% of SPOS's transferred to online services which makes sense and even after that um a lot of uh services they still uh continue to offer being online or hybrid and this shift was driven not only by necessity but also by accessibility preference among immigrants a fact that a lot of research showed and uh even IRCC's report newcomers outcome report in 2023 clearly uh showed this preference however when it comes to skilled immigrants there's a significant gap we all know that their needs are often different from the general newcomer population particularly around employment related services based on our experience in the sector I worked in the sector uh almost three years and based on the previous project that we had a three three-year project funded by IRCC Rosby remote and online services by skilled immigrants we know but by fact that despite digital literacy among skilled immigrants there are relatively few online services tailored specifically to their professional integration so while digital literacy expand broadly skilled immigrants continue to face a gap in services specifically again in employment service support highlighting a key opportunity for innovation in the sector and when we talk about uh innovation sorry it's really difficult to not think about and talk about AI based on our firsthand experience experience working with a skilled immigrants we see clear potential for AI to support them in very practical and meaningful way take credential rec recognition for example the process itself can take months and even before that immigrants often spend months just figuring out where to begin imagine a dentist trying get license in Canada a trained AI can could provide clear step-by-step road map showing exactly what courses exams or applications are needed and in what order that's a huge timesaver and of course uh a stress reducer or uh job skill matching is another important uh potential for AI that can make a real difference job titles in Canada are often different and there are more options that newcomers can realize i remember uh when I came to Canada almost 3 years ago I started searching job just in academia which was really difficult because I didn't have Canadian work experience in academic context or even I didn't have any uh Canadian uh credential so then I realized okay there are other organizations that they need my research skills so AI could help immigrants discover all the roles that they could fit into highlighting transferable skills that they might they might not even realize that they have or personalized learning and reskilling is another exciting possibility for example an engineer wants to apply and get licensed by EPA uh they don't need to spend hours searching YouTube or scatter resources ai could could generate much lessons provide targeted practices and create a personalized training pathways based on the exact gap in their knowledge or experience so overall AI isn't just a tech uh buzz word uh it offers very practical ways to bridge the gaps skilled immigrants face and to support their full potential in uh to be integrated into the Canadian labor market so with that with all of the challenges that the skilled immigrants face and growing need to fully utilize their um skills especially with the recent Canada's immigration shift to attracting more skilled immigrants compared to other immigration categories we decided to take a closer look we conducted a case study to evaluate two digital tools one AI powered and one nonAI powered our goal was to understand their effectiveness in supporting skilled immigrants labor market integration and specifically we wanted to answer to these three key research questions how effective are these tools in enhancing the skills that they need to be integrated into the labor market what are the primary challenges that a skilled immigrants face when they navigate these tools and another important part was that we wanted to know how do developers of the tool evaluate the cost effectiveness and return of an investment of the tools so uh for this study we looked at two digital tools one AI and one nonAI names kept confidential the first one is AI powered helping a skilled immigrants to match with jobs uh optimize resume and track trending opportunity and the second is a nonAI settlement uh platform basically a one-stop resource for navigating life and employment in Canada together the these tools let us compare uh AI job uh support with broader settlement services for skilled immigrants uh for this study we use a qualitative research method with semistructured interviews and we focus on two group of participants the first group was a skilled immigrants the actual users of the tools we conducted sessions of 90 to 120 minutes uh with them during the sessions we ask them to navigate each of the tools 10 to 20 minutes and after that we ask questions about the challenges that they face uh what they like what they uh really didn't like and Oh I think you muted yourself by mistake uh it's a stranger I didn't touch anything okay okay so uh I'm not sure where was the last point that I was muted it took just a just a second um may I also just uh the two of you just let you know that it's been 12 minutes um so just thank you this is almost the last slide yeah and uh so the second group was stakeholders and tool developers we included them to understand the story from their side how they designed the tools uh what challenges they anticipated and where they expected the tools could have the biggest impact in total we interviewed 15 skilled immigrants and five stakeholders and uh bringing together both perspective gave us a kind of comprehensive pictures of how the tools work in practice and how they were designed to meet users need and the last slide okay sorry and we've just completed the interviews and there are transcriptions will be started in the coming week based on my experience during the sessions some early patterns are already emerging we notice clear differences between AI and nonAI tools uh and it uh each seems to work better for different aspects of supporting a skilled immigrants we also some patterns related to the fields that skilled immigrants work and for example the some of in some of the fields they find it uh helpful in other disciplines it wasn't helpful and uh our next step would be conducting uh of course in-depth data analysis and from there we aim to develop datadriven recommendation for the sector and on where and how AI can best complement nonAI tools tools to of course help a skilled immigrants to be integrated into the Canadian labor market thank you so much thank you so much i guess your presentation was so good it ended up being censored for like you know it was too powerful and too too excellent but jokes aside thank you so much for uh presenting i will now invite Maria Cvart Cvantes Masias um who's presenting a paper with Suzanne Uo to uh start our presentation and I remind everyone that we have a chat at the end where we can uh share our um our find our question yeah hi everyone can you see my screen no or no you're muted can you It's coming um I think you need to double click or something it's dark sometimes it just takes a couple of minutes ah there we go yeah okay perfect um okay well thank you all so much for being here um Maria already introduced me but I'm a post-doctoral research fellow at the Center for Migration Studies at JC um so first I just want to acknowledge that I'm coming to you from the traditional ancestral and unsea territory of the Muskian people and um it's going to be I guess a little bit of a departure from the previous presentation i this is a work in progress that I have been presenting in geography conferences so apologies if it's very very about scale and geography uh but I'll try to make it accessible to everyone um but what we're trying to do with this paper is to analyze how digital platforms and cananan immigration regimes are shaping immigrant workers trajectories um and producing different forms of procarity around space and like specifically urban spaces and then also new and access to labor opportunities and most of our field work was conducted in Toronto and Vancouver during last year and this year so the big kind of conceptual point that we're trying to make with this paper and with this ongoing research is uh the retritorialization of labor relationships so we're trying to think about the ways in which the digital economy and like the platform economy more specifically is impacting the multiscalar geographies of platform work um not only assuming that because this work is done online or through apps it's derritorialized but instead trying to argue that what we're facing is really a reterritorialization of space um meaning that there's specific social formations that come of a specific moments in space or like places um and then the other kind of outcome of this project is to try to study how immigrant workers are basically segmented into different labor markets and also trying to make a broader point that this is extremely class-based uh due to how immigration policies work so it's kind of obvious in a presentation about migration but like why should we study immigrant workers um one part of this and I think it resonates with some of the presentations from the panel before um is that there is an increase in temporary migrants that is may not necessarily matching the demand of the labor market so this creates what some scholars have called a surplus population that is then contributing to the ondemand economy while also adapting to achieve from standard employment which has like one directional employer to a non-standard employment relationship but there's no clear uh employer employee relationship and all of this also with like the temporary um aspects of immigration policy and an a second aspect of this is we don't actually know how many immigrant workers contribute to the digital economy not only in terms of gig work which is really hard to calculate precisely because of these non-standard employment relationships um we have a better idea of how many workers are for example software engineers etc so we're trying to see that whole picture of where are the immigrant workers in Canada as digital economy um in terms of our methodology we have conducted 45 interviews um in Canada 30 semi-structured interviews with remote workers most of them working in the tech sector and then 15 on the spot interviews with gig workers um I'm happy to talk more about this if it's of interest but methodologically it has been a challenge to recruit gig workers precisely because they are um always working so it's hard to find a time with them to try to get an interview so what I ended up doing was just walk around Vancouver um trying to reach out to them if they had like if they were like wearing backpacks to deliver food etc so most of these gig workers are um driving scooters or walking around the city or like evac so that's most of the picture that we have we also have some interviews with influencers some are doing cloud work where the majority are food delivery drivers um we also did an analysis of policy documents and then nonparticipant observation um and now to talk about the findings so first um I organized these findings in terms of scale just to contribute to this ret territorialization arguments that we're trying to make um so first looking at the national and the transnational scale at the same time I have conceptualized the Canadian immigration system in this kind of very mappy way um to try to see how it is differentiating workers just by the means in which they enter the country uh so for example if someone enters on express entry it's likely that they won't have any Canadian work experience but they will have a full-time work permit and also some kind of professional experience so it is more likely that they are going to work remotely although it is also possible that because they don't have Gandhian work experience they are going to then have a hard time finding a job if they didn't come with a job offer um then on the other extreme we have government assisted refugees who have full-time work permits but no Canadian work experience um and because of other things like language barriers and just priority in general they are more likely to exper to do geek work and in the middle we have international student migration which I'm trying to argue throughout my work that is extremely class so you have um students that come to very prestigious universities that end up doing co-op programs internships they are more likely to work remotely and find employment in uh the field that they are specializing in and then we have international students that come to other kinds of programs where they have to do gig work in order to sustain themselves to pay rent and afford the high cost of living of Canadian urban centers so they are most likely pushed into gig work and this is kind of like the background of um of the scale that we're working in in this project so I have some I won't spend too much time here but I have some examples from our interviews um Jones who is a remote worker I think a software engineer was telling us like I've been here for a long time i have the points but it's not enough so like and I'm making all this money I could just go back to India where he's from so he's also experiencing like this um kind of limitations of not having permanent residence and just trying to navigate how he is going to be able to stay or maybe have to go back um and then we have Carmen who came actually on express entry but couldn't find employment in her sector so she is doing gig work and she was telling me like well we have to kind of accept that we have to do entry- level positions even if I had a manager position before I moved here etc so it just how showing how the way that you enter the country is going to affect then your labor outcomes as well and then once we go into the urban scale or the city scale um we also see that there's only a few little cities that concentrate what is called the core jobs of the platform economy which is like software engineering tech sales etc and then also a peripheral jobs like gig work um but it concentrates in this urban center so that really contributes like the Vancouver Toronto aspects of this work and then these platforms are actively remaking scale by redefining what kinds of urban national transnational um and one of the biggest challenges I've had in Vancouver is that I know there's um this over called Richmond where there's basically like a parallel scale of a lot of gig workers from China you like Chinese migrant workers that use Chinese apps and because I don't speak any of the languages it's very hard for me to have access to this so it is remaking scale in a very explicit way while also all of this is happening in the Metro Vancouver area so that's something that we want to look um into as well um but we can see basically how people are pushed to make decisions on where to live not only after they enter Cana so for example John lived in Otawa but he couldn't find a job there that was like useful for him so he had to move to Toronto and then he had to move to Vancouver so he's just like making all of these moves in response to the labor markets um and he's a remote worker also in tech and then we had Brandy who is a gig worker and she was telling me more about like the very specific scales of Urban Toronto which I've never been to but I kind of got a sense just from like reading the interviews uh saying like "Well Bmpton is very saturated if I go there I'm just not going to I may may only make like $30 an hour for three or four hours of work but if I go somewhere else more far away then I might make more money and just like how these gig workers are responding to specific conditions of uh place in order to make decisions um and then at the individual scale I think the main argument that we're trying to make in this project as well is how the temporal forms of bordering are basically incompatible with the flexibility of the platform economy we see this most clearly with international students who are doing gig work because they are unlimited work permits so they can only work up to 24 hours a week uh but the count that they're waiting doesn't count as work so there's this tension between you're only allowed to work 24 hours a week but then they're always waiting on their phones and they might not be making enough money so it it is fundamentally incomparable these limitations are on work but then we also have examples of people who were able to obtain PR and then they're working more than full-time hours um so this on demand always on expectations of the platform economy is shaping the ways in which uh immigrant workers are kind of trying to interact with their work um and this is based on like the demands of what some authors have called the fulfillment or the algorithmic city so yes this idea of always being on um and here are some examples of this from our data so for example Matt was a software engineer and he works remotely and he has managed to have three full-time jobs um in the industry and he was like well I'm a little bit depressed but I just like I'm making so much money it's hard to say no if I just keep getting these opportunities um and it it's just like well you if you're not going to the office then you can just keep working all the time and kind of like organize yourself to make it work um and on the other hand Harpit who is a gig worker was telling me like well I was a software engineer back in India right now I'm doing a cyber security degree and I have to do gig work in order to pay my rent uh because I cannot get jobs in my field because I only have a limited time to work so they're being pushed into gig work because of these like temporal forms of bordering of immigration policies um so yeah in conclusion I'm not sure how much time I have left but basically what we're trying to argue in this ongoing work is that migrants work experiences are shaped not only by their personal choices but also by the digital platforms that are creating these work environments by the cities they live in and like the labor markets of them and then also immigration rules that sort them into different kinds of jobs once they enter the country or when they're transitioning from different permits to another uh and this creates very uneven opportunities and procarity across Canadian cities um and just to conclude we have I have co-created this uh infographic that resumes kind of all of the work on progress with a graphic designer named Sophie Doner so I'll just give you like two minutes for you to see it but I'm also happy to talk about this um this work uh and yeah thank you all so much and thank you for organizing thank you so much Maria uh it's really interesting and this presentation uh and this visualization is is uh very compelling so again for questions and comments we have time and uh I know some people are already using the Q&A uh section uh but you can also leave comments in the in the chat so we'll now uh invite uh Emir Bari to uh make his presentation who al is looking at another aspect of uh gig work can you see all right yeah okay so uh my presentation is titled misclassification migrant labor and new technologies in the Canadian longhaul truck driving industry and uh so I got into truck driving back around 2022 when I was doing interviews for my PhD thesis with actually Uber Eats couriers in Toronto and a lot of them were international students from India and uh a lot were saying that they were on a study permit and their goal was to um get a work permit to drive trucks and so I started digging online and I found a lot of uh Facebook groups Reddit threads uh and it was pretty shocking the the the working conditions in the truck driving industry for immigrant uh truck drivers so what I'm presenting today is uh my postoc research at the IRMS and it's actually the first time that I'm presenting these uh preliminary results from um from the field work that I did uh over the last couple of months so maybe first a quick uh word on logistics um so what are we talking about when we say longhaul truck driving or line haul uh truck driving well uh we have a lot of people uh ordering online now a lot of companies they need materials um and so the whole supply chain is based on this um logistics of uh moving goods right and so the the buyers and sellers of labor power they always need truck drivers to drive trucks with boxes in them um and so we have logistics companies transportation companies and just uh regular companies that needs uh that need movements we also have uh 3PLs which are third party logistics so basically the intermediaries that are organizing uh the the buying and selling of labor power so for longhaul truck driving here we see this like arrow that goes all the way around from uh a terminal to a destination terminal or a line hall which is just a a shorter uh distance um uh so this transport aspect of the supply chain is is where is what I'm uh currently interested in and the the key thing the key takeaway for for the the fieldwork right now is that over the last 3 years the demand has been pretty low because of tariffs inflation uh and so the demand for truck drivers has been pretty low so we've heard over the last two decades that we have a major labor shortage in truck driving and it is true that in some part of the country there are uh labor shortages for truck drivers but we also have other parts of the country where we have basically too many truck drivers and not enough uh job for them so so some key aspects of long haul truck driving are first of all the paper mile rates so truck drivers are paid like 30 cents 40 cents 50 cents per mile um they have long unpaid waiting times at terminals which can make um a a job either very worth it or very much not worth it um you also have sometimes unexpected maintenance costs on the trucks uh the brakes uh the tires the engine and this is often download downloaded to truck drivers and so the companies uh they're going to save if uh they can have their drivers agree to pay for some stuff on the trucks if uh anything breaks while they're on the road and also you have long time on the roads when you do long haul so uh you can have uh a job from Toronto to Texas or California or Vancouver and so you're going to stay multiple days sleeping in your trucks um and that's that's a huge um huge aspects of truck driving and you also have different types of of um of jobs of truck driving uh so a fully loaded a full truckload is going to be one company that is going to uh fill an entire uh box and then you also have the less than truckload so multiple companies are going to put boxes in the truck and then the truck is going to go through multiple terminals so this makes the the the the work um very uh segmented and very tough to um to find profits basically for these companies and so as we've seen in a lot of other sectors uh of the Canadian uh labor markets uh we've had a change we have multiple changes in the workforce and we've had this change towards precarious immigrants um so there's about 330,000 truck drivers in Canada and about a quarter of uh truck drivers have an nonofficial first language so this is how trucking HR Canada basically managed to look at how many uh quote unquote immigrant truck drivers uh there are and so Ontario is home to half of all truck drivers who uh say that they have a nonofficial first language first language being French or English and the most common uh would be here Punjabi with 30,000 so the vast majority um German Spanish and Polish uh but pretty far behind so it's important to emphasize the diversity of status and also the diversity of um of uh country of origin and so my research questions basically are around a couple of different aspects of these changes in the truck driving industry so first of all does mclassification as independent contractor contribute to the precarization of migrant truckers and if so how um we've seen the driver inc issue over the the last couple of weeks uh everywhere in the media and uh as I said the downloading of the cost to truck drivers often uh occurs through this uh mclassification scheme how do the policies the migration policies or labor laws influence the recruitment of truckers migrant truckers and allow for the lowering or even withholding of wages and so this is what I was mentioning with all these stories that I've been reading online and that that um I've had in my interviews and finally how was it excuse me i think it was a mistake you made going and then finally what is the role of new technologies in the logistics chains and inside the trucks for control and surveillance so this is u a final aspect of uh the various changes that we've seen uh new technologies they now play a huge role in the buying and selling of labor power but also the control and surveillance of truckers uh with new technologies inside the trucks so as I said uh I did I did a lot of um online analysis of various press releases um news media articles and then forum analysis so Facebook groups discussions Kora uh Reddit and I basically downloaded uh all of these discussions and uh analysis then I launched an online questionnaire where I got 202 immigrant truck driver uh response on basically on four different Facebook groups and then um I had a little section where they could put in their u email address if they wanted to do a longer interview and so then we did 36 uh semiructured interviews 28 with immigrant truck drivers and eight key actors of the trucking industry so trucking associations trucking HR um uh unions and also grassroots groups um so first you can see here on the right the out of the 202 responses um we had 7T open work permit truck drivers uh 58 permanent residents uh 41 temporary foreign workers and uh 21 refugees with 12 uh Canadian citizens so this is just a snapshot obviously it's out of the 200 responses out of the 300,000 truck drivers but um just to um highlight the sample that we were working with um and then with the country of origins this is just the top 10 but um 27 from Nigeria 20 from Ghana and then 18 from South Africa so um big uh African representation and then 12 uh from Mexico uh we had Australia France Philippines and the UK and then this is pretty interesting only six from India um so we can suppose that maybe the the Facebook groups that uh I launched the questionnaire on were just just not uh um were uh most Indian drug drivers would exchange like tips and and tricks and stuff so a couple words on the technological changes uh like I said the inward facing cameras uh was a big deal for truck drivers so a lot of them were scared of uh the invasion of privacy we also have new tools like facial recognition tools and um AI based fatigue detectors so in the name of like road safety the inward facing camera can recognize if your eyes are getting tired uh there's also like a constant hand surveillance so uh you can't pick up your your phone without um the like camera not noticing um we've had a platformization of the whole logistical change so the brokers intermediaries they play a huge role the 3PL's the third party logistics we have now basically the equivalent of like a Facebook marketplace for for for for jobs and um also the obvious like constant geot tracking of the boxes so a lot of companies they're going to want to follow their box so uh this is now a huge uh huge aspect of truck driving so a logistics company can offer this as a service basically and the LEDs which are the logging device where the truckers uh they log their their kilometers uh we've had stories with truckers uh having like two LEDs where so that they could drive more than the the the 10-hour limits so basically acting as if there were two truck drivers in the trucks even if it was just one and uh finally more on the like driving technologies so the speed limiters have been around for a long time but now we have these like blind spots uh detectors or like keeping the truck in line with the lines uh on the road the auto braking uh to cut down on brake costs and other like semiautonomous driving um but so the the the driver help systems what we've been hearing is that uh a lot of the best newest uh most advanced technology trucks uh they go to like the white Canadians and the immigrant truck drivers most of them they get the older trucks where the maintenance is like breaking down and the trucks is like in in a not great state um so it's there's a lot of uh unequalness in the the distribution of the the the technologies here and then finally on the working conditions so the the role of permits is very very important in truck driving a lot of temporary foreign workers uh that come here on u an LMIA a lot of companies they're going to charge for the LMAS it can be 40K 50K um and so this creates a relationship between the employer and the the truck driver even with if you're with an open work permit uh a lot of companies are going to use PR as a way to um get the truckers to drive more for less basically we've also seen a lot of um of truck drivers that are registering as independent contractors under Driver Inc even though they are temporary foreign workers which is supposed to be tied to an employer so they pay for an LMIA and then their employers are going to tell them register as driver inc and then you can drive for other companies and a lot of the time it's like a like other like u friends or c cousins or like a truck driving company can be basically just uh one person with two trucks and then finally we've had a lot of cases of racism and discrimination so a lot of truck drivers were complaining about uh very low pay for for for migrant truckers wage theft is something that we've seen a lot so kilometers disappearing from the LEDs a lot of overtime forced overtime because uh the pressure for these companies is like to drive the box as fast as possible to a destination very far a lot of u migrant truck drivers they say they're going to get the worst routes as well so the the the worst paying routes or even um long unpaid waiting times at terminals that are going to be not disclosed to the trucker and so the company is going to know that the there's going to be long unpaid waiting time but they're not going to disclose it and they're going to give these roots to um to migrant truck drivers and so all these illegal practices and this is my conclusion is uh we've seen a lot of planned illegalism as a way to gain a competitive advantage in the truck driving industry um and this is something that's been around for a long time but now uh it's changing towards uh the exploitation of immigrant truck drivers we've seen also this unequal use and distribution of the new trucking technologies towards more uh white Canadians versus racialized immigrants and this is something that is a huge deal for truck drivers because um as our survey was saying like most of the truck drivers were complaining about fatigue like long hours on the road it's a very demanding job and so having the the best most tech advanced truck is also uh something that really helps um for road safety too and so the various immigration pathways are always to consider and I've I put here this um this image from Lejon de Quebec where they're blaming basically Driver Inc as like this like taking time bomb of the trucking industry but they're not really considering all these aspects that put pressure on the truck drivers and this these pressures come from uh from all sides the employees the companies like paying for the boxes and they're for they're uh they have to drive like basically a lot uh of kilometers for very cheap wages so it's it's this whole uh system that we need to take uh into consideration and not just uh basically blame drivers like with the the Zed as if they're just like on their phone sleeping on the road uh yeah so that's it for me thank you and here's my contact if you have any questions thank you Emil um and uh I will now invite our last presenter but not the least uh CI Boru who's going to present uh a group paper on cyber security immigrant labor and the construction of racialized ideal worker just as a reminder everyone uh we have time at the end for Q&A um and you can always write in the chat or the Q&A tool so uh you may share your screens and can everyone see my screen yes okay hi everyone good afternoon uh I'm Sepid i'm a post-doctoral researcher at Toronto Metropolitan University and uh what I'm going to present uh is based on a collaborative work with uh Dr rupanery and Dr ari Mashhatan that uh focuses on um immigrants um labor market integration in cyber security uh the project is funded by bridging divides and Canada first research excellence fund um so I'm just going to quickly acknowledge the land that TMU is situated in which is in the dishwid territory which is a treaty between the anishab Misaga and Hudeni um so uh cyber security is an uh emerging uh high-tech field um within the high-tech industry that is mostly driven by um fast evolving technology and the threat of uh cyber attacks and the combination of the two often uh requires uh work devotion and um blurs work life balance for those who work in the sector um cyber security has um been long um struggled with labor shortage and to fill that gap uh the sector has been leveraging uh immigrant and foreign talent uh while uh there is not a lot of information about um the labor market and workforce dynamic in terms of race and immigration but the existing report shows that the sector has remained white and male dominant with non-white um employees or workers being minoritized still and racialized um woman um making up less than 5% of the workforce uh so having that in mind uh we try to look into how um cyber security work is shaped by um gender race and immigration status and how the unequal um work is also reproduced through the way employees themsel perceive uh their work and perceive themselves as ideal worker so we ask how does immigration status intersect with race nationality and gender to shape the work workplace experiences of immigrant workers specifically racialized immigrants in this paper and how do workers understanding of themselves as ideal worker contribute to maintaining the uh organization of work as it is uh this uh research was built on sociological theories of gendered and racialized organization um we specifically built on the existing literature on tech work that highlights how uh the hierarchy the workplace culture is often um defined by masculine and white norms of whiteness um and then we'll link that to the idea of um ideal worker and how um our participants are trying to approximate that image uh we also used um research on racialization and immigration uh specifically the literature on stereotyping such as um model minority stereotypes and um parallel stereotype in the way that uh shapes immigrants and the way they navigate their work uh we built on semistructured qualitative interviews with uh 49 participants uh they come from different backgrounds different racial uh backgrounds such as South Asian Latin East Asian West Asian and uh black participants um the um we use interpretive grounded uh theory approach to analyze our data uh which starts from open coding and then uh we identify the main codes so I'm just uh going through the main things that we highlighted in this specific paper uh we discussed uh how the organization themselves um create um divides within the workplace and later how employees uh based on their race and nationality of origin and gender uh reproduce the existing um hierarchies um so um cyber security uh often uh is understood as a sector that is multidisciplinary and acquisition of uh technical knowledge is more accessible through certifications uh however uh our participants and based on what we gather from our qualitative interviews uh there is a divide between technical and non-technical positions um and the positions that immigrants actually get channeled into or have more chance in getting um so um for uh uh our participant mentioned that uh non-technical positions are specifically unavailable to them or have been have been experiencing exclusion from those um positions such as consulting or technical sales um the cultural uh differences uh not having um the network to build a um client base a body of client base was impactful in um their exclusion but on the other hand for those specifically coming from countries like India like Iran like uh China who come with strong STEM background uh the only available option for them would be um technical jobs and um for women specifically non-technical jobs um racialized women specifically non-technical jobs have been unavailable because of um them being puted against white normative uh representations uh we had participants talking about how they have been scrutinized for not wearing makeup or not doing their nails properly or having accent um but on the other hand um one of our participant mentioned uh when they need technical jobs getting done when they need boots on the ground they go for racialized people who are such hard worker but um the what we try to highlight in this project is that um the same divide uh the same um hierarchy in the organization is often reproduced also by um participants as they are trying to fit in as they try to integrate into the sector um we also tried to to uh categorize that based on uh racial differences and nationality of origin asians um or pe people coming from Asia has been historically identified as model minority especially in tech industry because they often come from countries with uh technical backgrounds um and they are identified as inherently hardworking people our participants from Asian origin specifically adopt that norm um as they highlight their technical competency that they brought to Canada but also the resilience that they developed as uh as they they've been dealing with immigration challenges uh our participant mentions um that what distinguishes people like her immigrants uh from the Canadian ones is that they don't have roots here and they do a lot of work um because on the other hand Canadians uh cannot handle uh very stressful jobs like technical roles that require a lot of uh resilience they panic easily but on the other side of the model minority stereotype is the Asian peril asians have been historically been blamed whenever um crisis happened like co or in the case of our participants um it also varies based on their nationality of origin in cyber security or in technical uh roles in general uh for people coming from Iran or people coming from China with um but their nationality is identified as countries which are in conflict with Canada and they often get um attacks from countries like um Iran or China uh people feel uh very insecure about it uh so uh we had participants mentioning that people from these specific countries sometimes get uh excluded from sensitive projects or if they are on temporary work permit they don't even get to enter the sector uh one of our participants mentioned that having that um Iranian background always makes her cautious that feeling of insecurity nobody talks about it but she often carries that sense of insecurity with her although she is a Canadian now but on the other hand for people uh coming from India the idea of threat is very much associated with the current discourses anti-Indian discourses in Canada um so um one of our participants mentioned that in people coming from India they work until late 900 p.m because they are good at it they're very technical worker but on the other hand they can make Canadians resentful because they spo spoil the beautiful culture of work life balance so here what we also try to highlight is the gender impact because obviously in a male-dominated work workplace with masculine work culture even when even racialized men were having easier time integrating into the tech culture and assimilating while women often carry the insecurity of the stereotype um again um we try to uh discuss how the Asian model minority stereotype is inherently masculine while all women uh have been um having difficulties and barriers fitting into that male dominated masculine workplace it was basically reported from our Asian origin h participants that they often get the question of how many children do you have or are you planning to have children and one of our participants linked that to the stereotype that exists of brown women because there is this understanding that they cannot have work life balance because at home uh they don't have the stability or they have to shoulder the heavier workload and so not only h do they have to carry the care work that has been traditionally put on women but also they have to um deal with the stereotype of brown women not um being able to commit to work uh and for a lot of them having children means that their career will be stagnated for a while uh our participant mentioned that after coming back from maternity leave she felt like that uh she has lost the trust and she needs to prove herself all over again and gain back that trust um we try to distinguish um this group's experience from the groups that have been historically identified as not hardworking enough specifically uh black participants and Latin part and Latin American participants um our participants were specifically aware of the historical uh construction of not hardworking enough both our Latin and black participants and one of our participant mentioned that when people hear about Latin America they often con talk about oh it's fine gender nice bitches nice food but he identifies this as a microaggression because it feels like that he's being reduced to those type of things and at a larger scale um they don't see people from Latin American countries as um serious workforce like people from India um our other participants um link that to their nationality um one of our participant mentioned that coming from Nigeria not only has he you has he has to um carry the burden of black are not hardwork enough but also um Nigeria is constructed as a country that people are fraud and people commit a lot of scams um and he often gets this um jokingly comments that um hint at Nigerian prince uh but we try to also highlight how for women h carrying those weight are difficult and the only trajectory available to these people are uh lying on relying on uh work devotion and um the norms of mother minority uh so uh we try to uh contribute to we try to bring in cyber security to show the uh coexistence of both positive and negative stereotypes that not only applies to Asian population but can apply to uh other racialized group uh and try to highlight how uh those experiences can um vary based on nationality of origin thank you very much for the opportunity and look forward to questions thank you so much and thank you to all of our panelists so uh if um if uh or yeah if the friends uh at technology could make sure to pin and spotlight everyone so we do have a couple like two questions in the in the in the Q&A um and what I propose is that uh anybody in the room who would like to ask a question um can also raise their end their dig digital end we'll take a couple of questions and then maybe just have a round of answering those questions and then we can go on so while uh everyone is getting or becoming a less a little bit less shy I'll ask um the first question uh so the first question is for Anushia and Fami so uh someone asked whether you could elaborate on your prelim preliminary findings uh curious about which group and what AI tool you did a really good job that's me adding you did a really good job at convincing me about the project and now I want to know more um and uh I also see a question for Emil asking whether you looked at other countries particularly the US to compare how condition may differ especially as it relate to the impact of immigration status procarity and uh you know whether there's a possibility to uh talk to us a little bit about labor or labor organizing in relation to this so maybe we'll take our first two questions um uh answers to the first two questions and then we can see whether more are showing up i can answer to that question Anusha if you're okay yeah so uh one of the features of the AI tool was that the applicants or the users they could upload their resume and choose whether or not to customize the jobs that they want to apply so the tools then provided a list of the positions along with a percentage that indicate how much they are fit for that position and of course there was a section that they could uh customize your resume based on that specific job posting we had a participants with background in IT who could he who found the AI tools very helpful of course the first explanation is that because of the digital literacy but the fact is that um his foreign education and work experience were recognized in Canada so the job positions that the AI suggested for the participants made sense because uh okay this is something that I can apply for them on the other hand we had a doctor immigrated to Canada two years ago who practiced uh in her own country and uh working in a completely different context so and the same approach when AI suggested some positions most of them were related to the healthcare management from her perspective all of them were were in unrealistic so that was a huge gap and uh the tools that we started evaluating so it wasn't just for the sake of evaluating the tools we wanted to know with the with the practical tools what a skilled immigrant need from a tool so they needed that AI could recognize okay I have a background in medicine in my home country and for being a doctor or working in healthcare in management position I need to have Canadian work experience in healthcare so which means that they was not as smart as we expected so this was a kind of the fi one of the findings of course everything that I'm saying all of us PR researcher and these are the preliminary patterns that we observed the moment that they start to analyzing the data and looking at each of these relations definitely I I'm sure that I will find more interesting patterns yeah thank you uh may I open uh the floor to Emil yes thank you um yeah so obviously during my literature review I've had a lot of articles from the US um a lot of drug drivers they're working across border like north south obviously there's also a lot of east west in Canada but mo mostly it's north south um so there's a there's a lot on uh Latino workers in California uh Turkish migrant truck drivers in the tri-state area um and they were basically finding uh that the path not once you are a truck driver is either to become an employee to become an independent contractor or u to uh move to another state to basically try to purchase your own truck start your own business and so on um and there's also lot yeah a lot of labor organizing efforts obviously uh the team stars in Canada are the unions most implicated in truck driving u their position on technological change is is similar to those of truck drivers basically like it's very good for road safety uh what about invasion of privacy if the inward facing camera is always on even when you're not driving this might be an issue um but again the technological changes in trucks uh it mostly happens in big companies because they have the money to uh basically buy all this tech for their their truck fleet right and so the smaller mediumsiz carriers they they might not have the the same um technological transformations as as the big carriers like uh Transforce or uh FedEx and so on um so yeah you you basically need need money to uh to have these tech transformation and maybe like a quick word on Maria's question on uh well I think the Nigerian over representation might have to do with like a WhatsApp group chat for Nigerian truck drivers and maybe they passed on the survey there and that's why we've had like 30 uh because uh I did interviews with Nigerian truck drivers over the last couple of years and a lot of them were telling me about the WhatsApp uh group where they're exchanging like tips and tricks and so and so the for the for the Indian truck drivers uh again it might just be other Facebook groups that the ones that I posted the survey on so uh yeah that's it for me thank you all right um may I ask a question of Sapid uh just wanted to to ask whether uh you and the team are thinking about expanding this beyond uh uh security cyber security to other tech sectors so I think there's like two things that I I feel like we're talking about or we're talking about the specificity of the sector but then also things that I think are probably more like broader than like uh the sector uh so that would be one question uh for you and then uh if possible for Maria um also wondering about um whether you expect that you know in the cases like uh Richmond where you haven't been able I know it's it's it's hard to know but like whether uh you think that some of the dynamics that you notice in like larger urban center might be a little bit different for gig workers when they're working in closed loop kind of ethnic based um uh gig Um thank you for your question so uh I think yes a lot of qualities of work and the characteristic of employees experiences um matches with high-tech itself um with cyber security it comes the notions of trust and um who is trusted who is not trusted but also as a very fast growing sector that is and because some of the jobs in high-tech is now being saturated at least in Canada uh I think even in um IRCC um categorization of in demand jobs we don't have a lot of uh take jobs anymore cyber security still is there like people can h apply through cyber security um stream or h as um in demand job but a lot of STEM related or um techreated jobs are now saturated but considering if we have thought about it or not I will leave it to Rupa or add it to uh as the PIs of the project to kind of um add to that i'll say uh can can you hear me i know my audio is not great um I'll say that um it is definitely something we've thought about but I think for this particular project from my perspective and I'll let Addie chime in uh because we haven't coordinated but um for this particular project I think the specificity of cyber security is important and is actually the gap in the literature as well um there's quite uh you know there's a growing literature and historically there's been a lot of work on technical jobs versus not technical job non-technical jobs and how immigrants fare in each but there's this uh militarization aspect of cyber security and I think that was also a really important kind of uh nuance that we wanted to focus on for this particular project so from my perspective I think this one should be about cyber security but Addy go ahead i think you covered it i I can't say anything more thank you yeah um and I'll answer the question thanks Mar um I think it's interesting because there's like the Oh can you hear me yeah the Chinese ecos Okay perfect um the Chinese app ecosystem is very interesting because it seems like it uses WeChat as a very centralized platform that then has like aggregate platforms but then we also see kind of just around like other apps like Phantom and things like that so my sense and this is also related to another issue that we had is that a lot of this delivery like another type of gig work is done in cars so I just have less access to that because I'm doing like on the spot recruitment so in the Richmond case I think also it's way more highway so there's not that much spaces for like uh scooters or evac so it's going to have to be cars and then getting access to that population is hard because of that plus the language barrier plus the app barriers but it will be I think we're going to try to um get one of our RAS to maybe look at that for his master thesis so just building on more like community based research to have access to that population but but in in a more general sense um cars are also like a very big barrier to access gig workers that are not like on scooters and and ebikes so that's interesting yeah thanks i I will follow the lead of M i think she she's leaving she uh she was uh in some urge so anyway it's the end of the of the panel we are at 4 uh uh 4:15 Montreal and 5:15 in Alifax so uh I thank you all very very much uh I don't know if we have the time for a final word uh before we leave like 30 30 second final word so uh we can end uh the the panel with your final uh uh uh final thought so I don't know Emil if you want to add something any okay so I thank you very much uh for your time and participation and uh we hope that we can be in contact in the future and thanks the audience for all the question and uh yeah have a great organization thank you thanks bye bye have a good day