Bibliographic record
Abstract
To everyone who has crossed my path in life. ii ACKNOWLEDGEMENTS Without any uncertainty, I would like to express my gratitude to my supervisor Joelle Pineau. I am especially grateful for her scholarship during my two years of studies and for giving me the opportunity to present my work at the ICASSP con-ference in Prague. She is the most wonderful professor I have ever known, and she is always so generous with her knowledge and advice. Her guidance has improved both my research and writing skills and made me a better researcher. It amazes me as to how someone can be so outstanding but humble, strict but still always places the interests of her students as the top priority. I have learnt many life lessons from her. I also gratefully acknowledge support from the Natural Sciences and Engineer-ing Council of Canada (NSERC) and the Fonds Qúebécois de la Recherche sur la Nature et les Technologies (FQRNT).
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.110 | 0.107 |
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".