President, Canadian Intern Association
Bibliographic record
Abstract
We would like to thank our client, Claire Seaborn, President of the Canadian Intern Association, for giving us an opportunity to pursue a project that deeply resonated with us. Without her support this project would never have seen the light of day. Additional thanks goes to Dr. Bart Cunningham, our project supervisor, whose knowledge and insight were instrumental in developing this report. A number of people brought attention to our efforts and helped get our project off the ground. A big thank you is owed to Zoe McKnight, Lee-Anne Goodman, Chelsea Jones and Andrew Langille, among others, for their interest and for helping this story to be heard. We would also like to thank the hundreds of interns who took the time to share their stories, hopes and disappointments. Your experiences moved and inspired us to finish what we started. This report belongs to you. And special thanks are owed to our families and friends who encouraged us and tolerated our many frustrations with understanding and empathy. À nos familles et amis, un gros merci pour vos encouragements et votre soutien durant cette aventure. Finally, we would also like to thank Cassie for her remarkable patience and quirky sense of humour that helped us on so many occasions. Of all those close to us, she suffered far more than anyone else. Now that this is all over we promise to make it up to her by treating her to frequent walks in the park and all the games of fetch. Page | iii
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.475 | 0.235 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".