This study was conducted by the Working Group on Library Instruction of the
Bibliographic record
Abstract
Universities (CREPUQ), in cooperation with several other parties. We would like to thank the library Directors and Registrars of the participating universities for their invaluable assistance in the selection of the participant sample. We also acknowledge the support and active collaboration of the Vice-Rectors of the libraries at the participating institutions. Thank you also to Normand Martineau, Educational and Research Counsellor at the Cégep du Vieux-Montréal, and Gary Wilson, Chair of the Research and Development Committee at John Abbott College, who kindly authorized the Working Group to pretest the questionnaire with students at their institutions. The Université de Montréal and Hewlett-Packard generously sponsored the computer offered as a prize to encourage participation in the survey. Their support is greatly appreciated. We would also like to express our sincere appreciation to all the students who completed the survey questionnaire. The present study is the result of the collaborative efforts of Dr. Diane Mittermeyer, Associate Professor at the McGill University Graduate School of Library and Information
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".