Bibliographic record
Abstract
When approached to write a commentary for this volume, I was very excited to be involved because I value failure.Let me explain.I am a firm believer that we can learn more from our failures than from our successes.I say "can learn more" instead of "do learn more" because, too often, we miss this valuable opportunity, focusing instead on the frustration, defeat, misunderstandings, recriminations, and justifications that are part of the experience.But failure, or "mistakes" more generally, lie in the realm of knowledge, so if we are alert, we get closer to better knowledge.1 That was the promise and potential I foresaw in the contributions to this special 25th anniversary issue and why I quickly said "yes!" without knowing what the contents would be or who the authors would be.Let me give you an example of why I value failure and mistakes.During my college years at Johns Hopkins University in Baltimore, Maryland, I volunteered as a tutor for students from Baltimore's very poor inner-city neighbourhood.One of the elementary school students I tutored, Billy, needed help with math, particularly fractions.I would instruct him on how fractions worked, and then watch as he tackled a problem.Clearly, Billy was working hard and he always came up with an answer-but it was the wrong answer.I tried instructing again, he tried calculating again, but again the result was the wrong answer.So I switched the roles: I let Billy teach me his method of working with fractions.His method was very systematic and thoughtful but it was not the "right" method.Once I understood and could reproduce his method (something Billy was quite pleased about, since he had successfully taught me), I was able to see why he did what he did and what caused his errors.Once we were anchored in his "mistakes," we were able to move together to the correct way to handle fractions.It was his mistakes, once we focused on them, that led Billy-and me-to better knowledge.
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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 | 0.115 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".