From “Canadians First” to “Workers Unite”: Evolving Union Narratives of Migrant Workers
Bibliographic record
Abstract
Federal government policy changes in the early 2000s led to the rapid expansion of Canada’s Temporary Foreign Worker (TFW) Program by increasing the number of eligible occupations. Before the expansion few trade unions in Canada had interaction with TFWs, but with the new rules, and the high profile political debate that ensued, unions were forced to confront the issue of migrant workers directly for the first time. Using narrative analysis, the paper examines media statements from union officials between 2006 to 2012 to track the narratives constructed by unions regarding TFWs. It finds three temporally sequential narrative arcs: 1-prioritizing of Canadian workers’ interests and portrayal of TFWs as employer pawns; 2-TFWs as vulnerable workers needing union advocacy for their employment and human rights; and 3-post-economic crisis conflicted efforts to integrate Canadian and TFW interests. The changing narratives reflect evolving union reaction to the issue of growing use of TFWs, as well as interaction with external political and economic contexts shaping the issue. The study examines how unions understand challenging new issues. The results suggest union discourses are shaped by the tension between internal pressures and external contexts. They also suggest that leaders’ responsibility to represent members can sometimes clash with unions’ broader values of social justice. Unions build internal value structures that inform their understanding of an issue, but they must also reflect members’ demands and concerns, even if those concerns may not reflect social justice values. The case study reveals the line between “business union” and “social union” philosophy is fluid, contested and context dependent. The paper also links union narratives of TFWs in this contemporary setting to labour’s historical attitude toward immigration and race, finding elements of both continuity and disruption.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.051 | 0.027 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".