Bibliographic record
Abstract
The W(D) decision matters.As a paradigm of the core principles of fundamental justice, W(D) has empowered the credibility assessment and given it meaning.From its release in 1991, the essence of the decision, invoked by the case initials, reverberated through the appellate and trial courts and changed the legal landscape.From its modest beginnings as an admonishment to beware of the impermissible "credibility contest," W(D) radically transformed the everyday to the infra-ordinary by imbedding the presumption of innocence and the inextricably connected reasonable doubt standard into the decision-making analysis.But the revolutionary path has not been easy as the courts struggle with the tension between the "ideal" and the "real."Yet, W(D) has survived this ordeal to become an essential trial concept.How W(D) has made this not-so "magical" transition is discussed in this article as we trace the impact of the decision through statistics, case law, the judicial lens and the personal perspective.At the end of this examination, we will see W(D) anew; not as a worn-out overplayed "mantra" but as an invigorating principle representing the plurality of what is at stake in a criminal trial.To apply W(D) is to know it.This article attempts that very task.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".