Bibliographic record
Abstract
While I hold that fateful wave fully responsible for setting this project in motion, I, too, hold responsible all those who have provided their support throughout this project.Without them, this could never have succeeded the way that it did.As the author of this book, my role was actually minimal.Th ose who have invested fi nancial support, provided supervision, built networks, gave their warm beds, or gave their not-so-warm couches, and who always encouraged this project to move forward are the ones who have truly made this study possible.I will take a moment here to give them all well-deserved acknowledgements and thanks and to explicitly state that I hold them accountable for seeing this through.To the Pierre Elliott Trudeau Foundation: thank you.Th is foundation off ers fi nancial and networking assistance to scholars to engage Canadians about pressing social issues.Its support, dedication, and encouragement are refreshing ingredients in how to approach international research.Th ere is simply no way that I could have completed this study without the Foundation, and I will be forever grateful to them for it.Foundation President P. G. Forest, former President Stephen Toope, and interim President Fred Lowry have done so much for the Foundation and its members, and they have been personally involved in helping me to develop and initiate this project.I sincerely thank them for their time and encouragement.A very, very special thanks goes
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.266 | 0.245 |
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".