Bibliographic record
Abstract
Introduction Mary Ann FRESE WITT and Eric WITT: Retrying The Stranger Again Susan AYRES: The Silent Voices of the Law Karen C. BLANSFIELD: Law and Order: Exploring the British Legal System in David Hare's Murmuring Judges Jenifer CUSHMAN: Criminal Apprehensions: Prague Minorities and The Habsburg Legal System in Jaroslav Hasek's The Good Soldier Svejk and Franz Kafka's The Trial Gwen McNEILL ASHBURN: Silence in the Courtroom: Language, Literature, and Law in The Ballad of Frankie Silver Deborah HECHT: Representing Lawyers: Edith Wharton's Portrayal of Lawyers and Lawyering In The Touchstone and Summer Eric STERLING: Ritual Murder and the Corruption of Law in Bernard Malamud's The Fixer Beth WIDMAIER CAPO: How Shall We Change the Law?: Birth Control Rhetoric and the Modern American Narrative Joseph SUGLIA: Putting God on Trial: The Relationship of Kafka to Leibniz Brian CONNIFF: Mumia Abu-Jamal's Live from Death Row as Post-Legal Prison Writing Ana Maria FRAILE-MARCOS: The Letter of the Law and Canadian Letters: Joy Kogawa's Obasan Alicia RENFROE: Prior Claims and Sovereign Rights: The Sexual Contract in Edith Wharton's Summer Nancy LAWSON REMLER and Hugh LAWSON: Situating Atticus in the Zone: A Lawyer and His Daughter Read Harper Lee's To Kill a Mockingbird Gwen MATHEWSON: Challenging the Court: Charles Chesnutt's Marrow of Tradition About the Authors
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.021 |
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".