Structured Deliberation for Dynamic Uncertain Inference
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
Dynamic uncertain inference is the formation of opinions based upon evidence or argument whose availability is neither disclosed to the analyst in advance nor disclosed all at once. Normative accounts of belief change, which work well when the analyst has prior notice of well-designed experiments and their possible outcomes, may not be applicable to less tidy occasions of inference. In addition, there is the clerical challenge of keeping track of what has been observed, what relates to what, and how. This Article begins with a discussion of subjective valuation in general. An approach to deliberation, similar to what is practiced in the multiattribute utility modeling community, is then suggested for dynamic credibility assessment. Features of the proposed technique are explained through their application to a celebrated French murder investigation. The method presented here may be reconciled with Bayesian belief models by noting that the latter lack a consensus view of how stable beliefs form in the first place. Thus, the ideas discussed here may be taken as an account of original belief formation, and so complementary rather than antagonistic to subjective probability methods.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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