Bibliographic record
Abstract
In this work, positionality matters: I am a settler Canadian, and my family home is in the overlapping territories of the Haudenosaunee and Anishinaabe peoples.Te benefts that I and my family have experienced or accrued have come at the cost of the safety, freedom, and well-being of Indigenous peoples.I also lived in the territories of the Coast Salish people during my studies, and parts of this book were written in all of these territories as well as in the city of Leicester, England.Although I have lived in England for most of the past decade, I have continued to beneft from the setter colonization of Turtle Island in many ways.Taking the settler out of the settler colonial nation-state does not end the obligation to pursue justice and decolonization.It is my hope that this book supports making positive change in the places on Turtle Island that I have called home.However, the real work being done by Indigenous communities is far more important.From Idle No More to NoDAPL, Shut Down Canada, and 1492 Land Back Lane, it has never been more obvious that Indigenous peoples' movements are strong, vibrant, and increasingly successful.Black Lives Matter and movements for racial justice are continuing to force change in ways that no academic ever can; as I write, the United States of America is in well-earned and heroic upheaval over the police murders of George Floyd, Breonna Taylor, and a staggeringly long list of others.More and more brave folks are putting their lives on the line to fght institutional and systemic injustices.I hope that this book contributes
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.410 | 0.268 |
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".