(De)Constructing The “Lazy Indian”: An Historical Analysis of Welfare Reform in Canada
Bibliographic record
Abstract
Since their official inception in the mid 1800s, Indigenous-aimed welfare policies in Canada have presupposed and entailed a racialized subject: the “lazy Indian.” This paper highlights continuities in how Indigenous subjects have been constructed in welfare policy discourse from 1867 to the present. Building from this historical overview, we analyze how today’s neoliberally inflected federal welfare regime at once recodes and reinscribes preexisting ethical narratives of “productive” and “unproductive” citizens, effectively casting Indigenous peoples as non-workers and thus “undeserving” of welfare relief. As our analysis indicates, further reform of welfare policies for Canada’s First Nations must first puncture the persistent myth of the “lazy Indian” in order to attend to the lasting legacy of colonial governance, contemporary barriers to self-sufficiency, and ongoing struggles for politico-economic sovereignty.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.041 | 0.024 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".