Bibliographic record
Abstract
advisors and great people. They have guided me through the last five years with skill and patience, and I am the better for it. Working with them has taught me the importance of attention to detail, of clarity of thought and expression, and of not mistaking the trees for the woods. Thanks also for the opportunity to spend a year in GT Lorraine, France! I also thank Dr. Gordon L. Stüber, Dr. David G. Taylor and Dr. Thomas D. Morley for serving on my defence committee. Thanks are also due to Zak Keirn and German Feyh for the invaluable opportunity to work with them in Summer 2003. The work was great, and so was the chance to be in Colorado! Now, things start to get a bit murky, what with so many other people to thank! As with anyone else and to an even greater extent, I have been helped and supported along the way by a great number of people and it is impossible for me to thank everyone of them without the risk of having the acknowledgements section as the biggest portion of this document. So, the following is a partial list of people whom I thank in the most heartfelt way. Thanks to Dilip, Antony and Clement for being who they are. I learnt an enormous
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.377 | 0.239 |
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".