Data from the Pan-Canadian Survey on Agent Use................................................................... 7
Bibliographic record
Abstract
under contract to the Council of Ministers of Education, Canada (CMEC). The authors would like to thank Monica Kronfli at CMEC for her unflagging guidance and support. We would also like to thank the many educational administrators and government officials who participated in the survey and interview phases of the study. The information they provided was invaluable to us, and we hope that, in turn, this report is of use to them in their work. Lastly, we would like to thank Dr. Ann Austin of Michigan State University for her assistance in developing the survey instrument and navigating the institutional review process. The opinions expressed herein are those of the authors and not necessarily those of CMEC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.010 | 0.008 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.042 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.048 | 0.119 |
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; both teacher heads 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".