The construction of the ‘immigrant’ in Canada’s immigration discourse : a Foucauldian critical discourse analysis through postcolonial lenses
Bibliographic record
Abstract
In this dissertation, I investigate how 'race' and 'ethnicity' relate to non-white immigrants' socio-economic marginalization, such as job ghettoization, underemployment and unemployment.Over the last three decades, the gap between immigrant and non-immigrant population with regards to socio-economic outcomes has been steadily growing (Block & Galabuzi, 2011; Reitz, 2011a;Thobani, 2007).At the same time, the proportions of non-white immigrants to Canada have been increasing.Currently, over 80% of immigrants to Canada come from regions with non-white majority populations (Statistics Canada, 2009; 2014a).I analyze the present immigration discourse based on Foucauldian poststructuralism (Foucault, 1971; 1972;1981) and postcolonialism (Said, 1978), to problematize contemporary societal and political engagements with 'race' and 'ethnicity'.Through a discursive review of Canada's past, I show how concepts such as 'visible minority', 'multiculturalism' and 'Canadian work experience' contribute to the marginalization of non-white immigrants, ultimately racializing them.I also conduct a Foucauldian critical discourse analysis (CDA -following S. Jäger, 2004; S. Jäger & Maier, 2009) on selected 'texts'.I show the colonial and binary dynamics at play in the image construction of non-white immigrants in the texts from politics, society and media.This dissertation contributes to Management and Organizational Studies (MOS) by shedding light on the taken-for-granted nature of discursive practices in organizations and contributing new insights into the current challenges that immigrant populations face.Finally, I show how theorizing about rather abstract concepts such as power, knowledge and discourse can serve as a framework to very 'practical' and 'real world' issues, thus making a strong case for how in-depth theoretical elaborations can serve very 'pragmatic' research inquiries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.073 | 0.057 |
| Scholarly communication | 0.026 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".