Bibliographic record
Abstract
Goel. Our task was to review the Faculty as per the terms of reference provided. We had a full schedule of interviews over the course of our visit, including discussions with groups of undergraduate and graduate students, senior administrative staff, faculty members, Faculty Council members, as well as all the senior academic administrators from within the Faculty and cognate units. All of these discussions were open and frank and we would like to express our thanks to all of the members of the Faculty and University for their constructive cooperation and valuable input. The committee is especially grateful to outgoing dean Pekka K. Sinervo, who was a gracious host and helpful informant. We heard many comments about his excellent leadership during his term. II. Terms of Reference Because the Faculty of Arts and Sciences has recently (in 2004) undergone a complete external review, we were asked to consider a more focused brief: 1. The appropriateness and effectiveness of the Faculty’s internal organizational, operational, and governance structure. Is the current structure the best model for such a large and complex faculty within the context of the University of Toronto? 2 2. The appropriateness and effectiveness of the Faculty’s relationships with the other arts
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 | 0.010 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.168 | 0.110 |
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".