Bibliographic record
Abstract
This book is the work of my late mother, Sarah Whitecalf, who recorded her reminiscences for two friends she made late in life: Freda Ahenakew and Chris Wolfart.The recordings on which the present volume is based are highly personal.They were made on various occasions over the course of almost three years, beginning in March 1988 and ending in December 1990, less than a year before her death on 1 October 1991.Some of these recordings were made at my mom's in Saskatoon or at the late Freda Ahenakew's home at Muskeg Lake.Other interviews were done in the recording studio of the Linguistics Department at the University of Manitoba or during one of the many other trips on which my mom went with Freda.She was often in the company of her friends, including for example Cecilia Masuskapoe, Rosa Longneck and Grace Ahenakew, but sometimes also younger Cree speakers such as her
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.547 | 0.339 |
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".