Bibliographic record
Abstract
I once said to a colleague that after hearing hundreds of alarming accounts of conflict in graduate studies I would rather write a book than complete a PhD ... and here is proof!My perspective on conflict in higher education is that of a former litigator and a grad school outsider (LLB not PhD), who worked as a university insider, managing conflict and complaints at two large universities in Toronto, Canada.As a junior litigator, I learned quickly that clients wanted us to take actionto advise them about how to proceed based on the facts, the law, and the realities of a given situation.When I was hired by the University of Toronto in 2015 to bring an idea for a conflict resolution center for graduate students to reality, I also knew action was required.The university was keen to support students navigating challenging supervisory (advisory) relationships -an issue that had already been flagged as a key concern in various surveys, reports, and by graduate student working groups. 1 I recognized that the scope of graduate conflict was likely much greater than supervision based on my experiences reviewing hundreds of student complaints with a fairness lens in an ombuds office ("an ombuds" is also referred to as an ombudsman or an ombudsperson).I saw potential for this new office to wrap support around, and develop capacity throughout, the graduate community.The resulting Graduate Conflict Resolution Centre (Grad CRC) included a diverse, multidisciplinary, graduate student peer advisor team.Training for the graduate peer advisors was intensive and comprehensive, and I extend my profound gratitude to colleagues who shared their time and expertise with the team -their involvement enhanced skills and profoundly impacted the team year after year.Over four and a half years we pushed the uncomfortable reality of grad school conflict into the open -challenging the graduate community to From the Author
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.242 | 0.166 |
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".