Bibliographic record
Abstract
In his 1827 work Rationale of Judicial Evidence, Jeremy Bentham famously argued against exclusionary rules such as hearsay, preferring a policy of “universal admissibility” unless the declarant is easily available. Bentham’s claim that all relevant evidence should be considered with appropriate instructions to fact finders has been particularly influential among judges, culminating in the “principled approach” to hearsay in Canada articulated in R. v. Khelawon. Furthermore, many scholars attack Bentham’s argument only for ignoring the realities of juror bias, admitting universal admissibility would be the best policy for an ideal jury. This article uses the theory of epistemic contextualism to justify the exclusion of otherwise relevant evidence, and even reliable hearsay, on the basis of preventing shifts in the epistemic context. Epistemic contextualism holds that the justification standards of knowledge attributions change according to the contexts in which the attributions are made. Hearsay and other kinds of information the assessment of which rely upon fact finders’ more common epistemic capabilities push the epistemic context of the trial toward one of more relaxed epistemic standards. The exclusion of hearsay helps to maintain a relatively high standards context hitched to the standard of proof for the case and to prevent shifts that threaten to try defendants with inconsistent standards.
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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.014 | 0.042 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".