Gender, race, bias and perspective: OR, how otherness colours your judgment
Bibliographic record
Abstract
This discussion considers assumptions about judges and judging and suggests that despite what is sometimes perceived as increasing diversity on the bench and in the legal profession, outsider decision makers’ membership of the jurisprudential community is still marked by ‘otherness’. The argument draws upon my ongoing interest in the law's concern with the concepts of ‘objectivity’, ‘neutrality’ and ‘perspective’. I argue that the legal system is inherently suspicious of ‘otherness’ and most specifically so when ‘others’ occupy positions of ‘judgement’. The consequence is to render decisions made by ‘otherised’ judges liable to attack for bias in a way that decisions made by insiders simply are not. The argument is illustrated by a review of a number of challenges made on the ground of ‘bias’ or recusal motions to judges whose failure to match the white Anglo hetero-normative standard of ‘the judge’ is seen as a limit on their ability to be ‘impartial’. The examples used range across many jurisdictions, from Australia, Canada, the US and a challenge to the impartiality of a decision of the International Criminal Tribunal for the former Yugoslavia (ICTY).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".