Bibliographic record
Abstract
I would first like to thank my supervisor Denis Thérien for his patience, support, and guidance over the years. His intuition and depth of knowledge have touched every part of this thesis. Denis has provided me the funding and the opportunity to work and study at McGill. I have thoroughly enjoyed my experience, and I’m sure the lessons I’ve learned here will stay with me throughout my career. I would also like to thank my co-supervisor Pascal Tesson, for reorienting me at the times when I felt the most lost, for regular invitations to Québec city, and for many rounds of thesis corrections. I would like to thank my program committee members Claude Crépeau and Doina Precup for their advice and support. I also thank my external examiner, Carlo Mereghetti, for many helpful corrections and comments. I wish to thank Patrick Hayden, Alexei Miasnokov, and John Liede for participating on my defence committee. I would like to thank my regular collaborator Martin Beaudry, for meetings in Sherbrooke and for his willingness to address the most technical of questions. I
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".