Bibliographic record
Abstract
Choices of terminology can sometimes matter a great deal in democratic deliberation, and it therefore seems necessary to address one of the book’s central terminological choices before it gets underway. Throughout, this book uses the adjective Aboriginal to refer to the first peoples of North America, coupled with a relevant noun—Aboriginal peoples, Aboriginal communities, Aboriginal scholars, and so on. This terminology is central to Canadian legal and political discourse, including the Constitution Act of 1982, where Aboriginal peoples are acknowledged as rights-holding entities. Since the bulk of the non-Aboriginal philosophical literature dealing with the politics of Aboriginal peoples in North America has arisen as a result of Canadian legal and constitutional debates over the past four decades, I have adopted this word choice throughout in hopes of increasing accessibility to that audience. Although this terminology is not widely used in the law of the United States, non-Aboriginal American readers are likely to be familiar with the terminology from these Canadian debates as well.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".