Bibliographic record
Abstract
All external links within this document were valid at the time of publication. Acknowledgements Thanks go out, first and foremost, to all Native literacy practitioners for the work you do. To those of you who completed surveys, attended gatherings, and otherwise offered guidance, support and honest criticism- Chi Miigwetch. It is not always easy to find the time, but many of you did so. This is the kind of support that works for the good of everyone in the field. The ONLC wishes to thank the Ministry of Training, Colleges and Universities and the National Literacy Secretariat for making this project possible. Within the field of literacy, the caring, commitment and dedication are very similar in all programs, yet it is the "shape " of the presentation and delivery, the cultural symbols and language, which make delivery effective and empowering for learners, staff and communities. The National Literacy Secretariat and the Ministry of Training, Colleges and Universities show their recognition of this truth through funding and field support for all streams and sectors of the Ontario literacy field. To many other colleagues and friends within and beyond the ONLC, the consultant offers thanks for your support and thought-filled assistance.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.797 | 0.675 |
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".