The geography of skill: Mobility and exclusionary unionism in Canada’s north
Bibliographic record
Abstract
This paper explores the spatial politics of racism and inter-worker competition through a case study of Indigenous employment during the construction of the Voisey’s Bay mine in northern Labrador. Over the course of construction, the building and construction trades unions (BCTUs) sought to restrict the hiring of local Inuit and Innu workers by challenging the legitimacy of place-based entitlements to work. Inuit and Innu workers had preferential access to employment as a result of unresolved land claims and the ensuing Impact and Benefit Agreements (IBA) between the Voisey’s Bay Nickel Company and both the Innu Nation and the Labrador Inuit Association. IBA provisions that local Inuit and Innu be hired preferentially ran counter to the unions’ organizational structures and cultures, which privileged worker mobility and skill. The BCTUs used the geographic incompatibility between the scale of Indigenous claims and that of construction worker organization to justify a competitive approach to unionism and to veil racist portrayals of Innu and Inuit workers. By drawing out the relation between skill, racism and beliefs about entitlements to work, this paper explores how workers selectively use place-based and mobile identities to participate in inter-worker competition, reifying colonial patterns of labour mobility and labour market segmentation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".