Bibliographic record
Abstract
I would like to acknowledge the essential connection that existed between the work of the Graduate Conflict Resolution Centre (Grad CRC) and the land on which the University of Toronto operates.For thousands of years this has been the traditional land of the Huron-Wendat, the Seneca, and the Mississaugas of the Credit.Toronto is home to many Indigenous people from across Turtle Island.I am incredibly grateful to the original stewards of this land and for the opportunity to live, work, and learn in Toronto.I have included in this book material related to the Grad CRC that was created or compiled in the course of my employment with the University of Toronto (2015-20), and with the support and funding of the University of Toronto.The case studies, examples, and excerpts from coaching conversations are based on actual conversations with graduate students, faculty, and staff between 2016 and 2020, with edits and compilations made to protect confidentiality and for illustrative purposes.The Grad CRC program partners were the School of Graduate Studies, Student Life (St.George), and the Graduate Students' Union (UTGSU) representing more than 19,000 graduate students on three campuses.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.181 | 0.135 |
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".