Bibliographic record
Abstract
It was at the Canadian Association for the Study of Indigenous Education in 2010 in Concordia University that I first heard from the women who were part of this study. They lined up in the front of the room in desks to share each their part of what would become the foundation of this book. The topic was their post-secondary education experiences becoming a teacher. Among them were a few of my Mi’kmaw relatives and long time friends. I remembered two of them as youth in Eskasoni when I lived there. The story they told was familiar. It was at the kitchen tables that I first heard about the experiences of these Mi’kmaq youth going to school off reserve. The traumatic events were regaled over and over, and everyone had stories to share about the racism they experienced taking the bus to the nearby town to go to high school, and the coached determination they received to continue going to school everyday. By the time graduation came each year, the buses that were filled at the beginning of the school year emptied out, and at most a handful of determined steadfast students graduated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 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".