Bibliographic record
Abstract
In criminal cases at common law, juries are permitted to convict on wholly circumstantial evidence even in the face of a reasonable case for acquittal. This generates the highly counterintuitive—if not absurd—consequence that there being reason to think that the accused didn’t do it is not reason to doubt that he did. This is the no-reason-to-doubt problem. It has a technical solution provided that the evidence on which it is reasonable to think that the accused didn’t do it is a different subset of the total evidence from that on which there is no reason to doubt that he did do it. It lies in the adversarial nature of criminal proceedings in the common law tradition that the subsets of the total evidence on which counsel base their opposing arguments are themselves different from and often incompatible with one another. While this solves the no-reason-to-doubt problem, it does so at the cost of triggering a second problem just as bad. It is the no-rival problem, according to which incompatible theories of the case based on incompatible subsets of the evidence cannot be rivals of one another. If neither party’s case contradicts the other’s then, by the burden of proof requirement, criminal convictions are impossible. Once having generated the dilemma, the object of the paper is to determine how it might be escaped.
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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".