“No other weapon except organization”: The Métis Association of Alberta and the 1938 Metis Population Betterment Act
Bibliographic record
Abstract
In the 1930s, the Métis Association of Alberta (MAA) successfully lobbied the provincial government to establish a royal commission to inquire into the socio-economic conditions affecting the Métis living in Alberta. The MAA strongly advocated that land be set aside so that the Métis could continue to pursue their traditional economic livelihoods of hunting, trapping, and fishing. Following the recommendation of the Ewing Commission, the provincial government passed the 1938 Metis Population Betterment Act, which provided for Métis land settlements. These lands represent the first time in Canadian history that a provincial government set aside land in response to Métis claims. The MAA and provincial government both agreed on the land grant, but for different reasons. The Métis were motivated by historical claims to redress failed government policies such as the Métis scrip program and to protect land rights from the further incursion of non-Aboriginal settlement. By contrast, the provincial government saw the land grant as an expedient and inexpensive way to distribute relief to one of the province’s poorest populations. This paper illuminates the Alberta government’s response to the political lobbying efforts of the MAA in the 1930s to address the question of why Alberta was the first (and only) Canadian province to set aside Métis land settlements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".