Bibliographic record
Abstract
As part of the legal test for bias, the courts have created a fictional fair-minded observer (the FMO) to act as a conduit for reasonable public perception. A number of scholars have raised concerns that the FMO bears no resemblance to an average member of the public or reasonably reflects general public opinion. This chapter presents our original empirical pilot study on expert versus lay attitudes to judicial bias. The study compares responses of legal insiders (lawyers and judges) and nonlegal experts with a basic understanding of the law (law students) to leading cases on judicial recusal. We use vignettes based on real cases from England, Australia, and Canada that dealt with different claims of judicial bias (covering issues of race, prejudgment, and more). The study may allow us to draw conclusions about the similarities and differences between legal experts and laypeople in relation to the perception of judicial bias, and we suggest ways the full study can address methodological limitations in the pilot that would allow us to draw those conclusions with greater confidence.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".