Bibliographic record
Abstract
1. Howson and Franklin's (1991) formalism 2. The Mendeleyev case 3. Howson's argument for predictivism 4. Conclusion ABSTRACT. Bayesian discussions of the value of novel predictions have become moribund since the early 1990's. The last major dis- cussion occurs in Howson and Urbach (1993), and the Bayesian po- sition on novel prediction presented there is largely negative. How- son and Urbach (1993) contains a defense of predictivism utilizing an argument found in Howson (1984; 1990). However, I show this de- fense to be unsatisfactory. Instead, by deploying a formalism found in Howson and Franklin (1991), and despite their disavowal of predic- tivism in that paper, I demonstrate in Bayesian fashion the value of novel predictions. The elegance of this formalism bypasses the in- teresting but rather complicated, Bayesian defense on predictivism found in Maher (1988; 1990).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".