Bibliographic record
Abstract
The article addresses the importance of the partnership between university professors and the Métis community. The Métis are a distinct nation and people that emerged in the northwest of what is now Canada and a bit into the United States through a process of ethnogenesis. The Métis Nation expressed its nationhood and defended its territory militarily in 1870 and again in 1885. Subsequently, Canada dealt with the Métis as individuals by implementing a scrip system, which displaced the Métis from their lands. In the 1980s and 1990s, the Métis Nation, along with other Aboriginal peoples, engaged in a constitutional process that witnessed limited success. Following that process, in 1993, the Métis moved their fight to the courts as many of their citizens were being charged with hunting and fishing infractions. This process necessitated the need for historical research and expert testimony, so the already emerging relationships with academia became more pronounced with progressive professors engaging in preparing expert reports and testifying at trial. Outside of the courts, research alliances were also engaged in. The outlook for the Métis Nation is more and more positive. The partnerships with academia has been a key contributor to this movement forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.035 | 0.015 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".