Accounting for Racialized Immigrant Workers: Historiography of the Colour Coded System of Disability Exclusion
Bibliographic record
Abstract
Canada’s quest and historical reliance on an immigrant labour force to sustain the nation’s economic and population growth are well established. This has been the case since the formation of New France, where the conquest of people and acquisition of lands and resources, in part, inspired the recording of the first North American census of 1666. However, record keeping practices that commenced in what is now called Canada were part of a larger scheme of a systematic power over governance structure in settler-colonial administrative edicts that cast the foundational structure for inclusion and exclusion criteria based on a colonial racist ideological model of who should be accounted for in the French settlement. As such, Indigenous People were not counted. Similarly, the records of people of Black African ancestry who were brought to New France through the inter-continental routes as enslaved labourers were obliterated from official administrative records. \nIn this first-of-a-kind historiographical query, primary and secondary records of racialized immigrant workers were examined to determine the extent to which Indigenous and racialized immigrant workers existed in governmental demographic records and other sources over time, inclusive of contributions, incurred injuries, disability and fatalities in labour force participation. \nA purposeful, cross-regional selection of Canada’s occupational health policy experts was engaged in semi-structured interviews on the question of demographic identity, including the intersectionality dimension of race and immigrant status. Insightful findings on the gaps in evolving race-based data collection in the nation’s contemporary occupational health and safety systems were garnered, revealing significant implications regarding the vestiges of colonial determinism in a perpetual colonial system, which underlies the absence of Indigenous and racialized workers from the nation’s informatics on occupational health and safety. \nUltimately, this project implicates an urgency for policy changes, aligned with the nation’s persistent reliance on racialized immigrant workers while relegating them to over-representation in work that is more dangerous and injury prone without visible accountability. Dismantling the current policy framework for health and safety by imposing meaningful strategic intervention measures and data collection on ethno-racial demographic identity in the nation's public reporting systems is the resounding outcome of this project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".