Bibliographic record
Abstract
A police officer sees a suspicious bulge in the pocket of a passing pedestrian and deliberates whether to stop and search. The pedestrian is also a young, black man, and from past searches and convictions, the police arguably know that such men are much likelier than other people to carry an illegal firearm. Should the police officer be instructed to take this information into account? This article objects to racial profiling because it relies on the following type of inference: from the individual’s membership of a certain racial group, the searcher is invited to infer that the individual is likelier to exhibit some culpable behaviour. The article shows that such an inference to culpable behaviour requires contradictory presuppositions about the freedom of the suspected behaviour. On the one hand, racial profiling ought to presuppose that the individual suspect’s behaviour is unfree because the inference it involves takes the suspect’s behaviour to be determined by his race, age, and gender, none of which is within his control. On the other hand, similarly to criminal trials, search practices ought also to presuppose the exact opposite: that the individual is free to determine his own behaviour. If the suspected behaviour is free, the involved inference to culpable behaviour is not probative of the individual suspect’s behaviour, so profiling methods which rely on it are useless. And if the suspected behaviour is unfree, the inference is probative, but the suspect is not culpable and should thus not be put to trial, whatever the profiled search yields.
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 | 0.039 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".