Bibliographic record
Abstract
We would like to thank all of the project participants. Their support for the project and the energy and commitment they brought to the various challenges we placed before them were inspiring and energizing. We would also like to thank our partners in this research project for their commitment and ongoing support: the Women’s Health Network of Newfoundland and Labrador and the Women and Resource Development Committee. The Women’s Committee of the Fish Food and Allied Workers Union also provided very important support. Colleen Hickey, Jodi Durdle, Katharine King were excellent research assistants who could be counted on to give us what we wanted when we needed it. The College of the North Atlantic made the initial contact with some of the participants and invited them to participate, and Human Resources Development Canada provided TAGS training data for our study area. The Newfoundland and Labrador Workplace Health Safety and Compensation Commission, particularly Charles Coady, met with us, answered our questions and arranged for us to get a set of data formatted for our needs. We would also like to thank the Ontario Workplace Safety Insurance Board for providing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.700 | 0.365 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".