Bibliographic record
Abstract
There are many individuals who have contributed to the success of this study and who need to be recognized for their contributions. Foremost, this study would not have been possible without the guidance of my thesis supervisor, Dr. Eric MacIntosh. His leadership, knowledge and endless support were extremely helpful and made the completion of this research possible. A big thank you goes out to the members of my committee, Dr. Norman O’Reilly & Dr. Benoit Séguin. Their knowledge and expertise were also very helpful and their feedback and recommendations played a key role in this study. I would also like to thank the members of the Ottawa Sports and Entertainment Group who gave me an opportunity to do this case study and were a pleasure to work with. An additional thank you goes out to my colleagues at the Canadian Olympic Committee who have supported me through these last few months and made the completion of this project possible. A special thank you goes to my supervisor, Jessica Bromley for supporting me through this process and teammates Andrée St-Éloi-Chartrand and Derek Covington for all of their extra efforts during my time away. Last but not least, a big thank you goes out to my friends and family for their endless
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".