Bibliographic record
Abstract
To get closer to a useful method of analyzing and evaluating witness testimony as evidence, we need to look more closely at what actually happens in trials. What typically happens in a trial is that when a witness is examined, the examiner will ask a series of connected questions all designed to probe into the particulars of some situation. The answers given by the respondent will tend to hang together in a coherent unity, sometimes called a ‘story’. The use of this term implies a certain skepticism, suggesting that the story may not really be true, and that it may be fabricated, like a fictional story. So when the examiner probes into the story, she may test out its coherence, as well as trying to just elicit further details. At any rate, it seems to be the story itself that guides how the testimony is evaluated as evidence (Bench-Capon and Prakken, 2005). The so-called story is really just the collected set of assertions forming an account of some supposed event reported by the witness. But since the witness is (presumably) in a position to know about the subject he is being questioned about, as shown in Chapter 1, this collected set of assertions can be filtered through argumentation schemes to provide evidence. Because appeal to witness testimony is evidence, presumably based on a rational form of argument, conclusions can be drawn from what the witness says.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".