Bibliographic record
Abstract
A journey is always easier when traveled with others. I have been accompanied and sup-ported by many people throughout this work. I am pleased to have the opportunity to express my gratitude to all of them. First and foremost, I would like to acknowledge a great debt of gratitude to my thesis advisor, professor Doina Precup whose inspiring and thoughtful guidance, and supervi-sion made my thesis work possible. Discussions and regular meetings with Doina always helped shaping my thoughts and motivated me to work hard. During the past several years Doina has imparted innumerable lessons from practical instruction on teaching to research, writing, and presentation. I am honored to have had the opportunity to work with such a smart, knowledgeable, dedicated, and principle-centered person. Working with Doina was fruitful and enjoyable at the same time. Thank you does not seem sufficient but it is said with appreciation and respect. I would also like to thank the members of my PhD committee who monitored my work and made efforts in reading my reports and providing me with valuable comments. I would like to thank professor Gregory Dudek for accepting to be in my committee despite his extremely busy schedule and for the extensive comments on my work; professor Monty Newborn who kept an eye on the progress of my work, was always available when I needed his advice, and has always built up my confidence; and professor Joelle Pineau whose expertise and constructive comments through discussions and meetings have had a direct impact on the final form and quality of this thesis. I am grateful to my thesis external examiner, professor Brahim Chaib-draa, for gener-ously spending time and energy in reading my thesis and providing valuable comments and constructive suggestions. ACKNOWLEDGMENTS Within McGill, I have been fortunate to have been surrounded by gifted minds and accomplished people. I would like to thank professor Sue Whitesides the director of the
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.252 | 0.212 |
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".