Scripts, Stories, and Anchored Narratives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it