Bibliographic record
Abstract
Over the past two decades, Métis scholars have called for a more Métis-centred scholarship.In 2024, we are positioned at Pawaatamihk: Journal for Métis to encourage and lift up Métis-centre scholarship.Due to the increase of Métis thinkers in the academy and in the community, we see an increase in Métis-specific knowledge production.However, it is essential to remember our not-too-distant past within the publishing world to ensure forward movement toward the vision many have expressed in their scholarship in recent years. How Did We Get Here?Isaac (2016) called for a "greater understanding of Métis distinct issues" (p.26) in his report on Métis reconciliation.Métis rights extend beyond land claims to inclusive scholarship.Historically, Canada has "downplayed Métis indigeneity or only recognized Métis rights and title to extinguish them" (Gaudry, 2018, p. 1).Madden (2015) asserts that Métis have been excluded from Crown consultations on their rights and denied access to programming despite including Métis in section 35 of the constitution, which recognizes Métis as Aboriginal people.Métis exclusion is a form of discrimination, and the lack of our inclusion in research and publications was historically due to the assumption that we fit nicely under the Indigenous (First Nations, Métis and Inuit) umbrella (Forsythe, 2022).
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.026 | 0.039 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".