Bibliographic record
Abstract
Who speaks and with what authority, who is believed, what evidence is introduced, and how it is presented are all informed not only by the substantive law and the rules of evidence but also by the rituals of the trial. It is from this legal process as a whole that a judge or jury determines the (legal) ‘truth’ about a woman’s allegation of rape. A sexual assault complainant’s capacity to be believed in court, to share in the production of meaning about an incidence of what she alleges was unwanted sexual contact, requires her to play a part in certain rituals of the trial. Many of these rituals are hierarchical, requiring complainants to perform subordinate roles that mirror the gender-, race-, and socio-economic status-based societal hierarchies in which the problem of sexual violence is rooted. Relying on the work of Robert Cover and interdisciplinary work on ritual for its conceptual framework, this article pursues two objectives. First, it attempts to depict, through the use of trial transcripts, the brutality of the process faced by sexual assault complainants. Second, it exposes some of the institutionalized practices, as manifested through courtroom rituals, that contribute to the inhospitable conditions faced by those who participate in the criminal justice response to sexualized violence.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".