Bibliographic record
Abstract
Chapter 4 focuses mainly on the evolution of the truth defense in Massachusetts; in Nova Scotia truth was a minor chord. In Massachusetts civil cases, however, judges systematically sought to contain truth and make it risky to plead, circumventing clear legislative intent to expand the reach of the truth defense. Special pleading was a casualty of this process. Similarly in criminal cases, judges ensured that a person accused of libel had to prove both truth and “good motives and justifiable ends”: neither well-intentioned but false, nor true but ill-meant statements would be protected. The key dynamic shaping this evolution was tension between reformers – abolitionists, temperance advocates, antimasons, Methodists and others – and more established, respectable men who did not wish to see their wives, children and other household subordinates drawn toward causes or imbued with knowledge of which they did not approve. Revealing unpleasant truths about reformers’ characters was much more palatable to courts than the same sorts of disclosures about those who defended orthodoxies. The idea that good intent was enough to save an accused person and that the defense could lead evidence of truth that might surprise the prosecution both fell, when it appeared they might save a reformer.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".