Legal institutions, nonspeaking recipiency and participants' orientations
Bibliographic record
Abstract
In this article, I explore the question of what constitutes a participant's orientation to gender within conversation analysis (CA), suggesting that CA's notion of participant orientation may be too narrow and restrictive to adequately capture the significance of gender as an organizing principle of institutions. The data that I analyze are drawn from legal settings, specifically a Canadian criminal trial dealing with sexual assault. Significant to an investigation of talk-in-interaction in such contexts is the fact that participants' orientations to the talk are not only discernible in the talk's local sequential properties but also in the (nonlocal) assessments and judgments of the nonspeaking recipients, the jury or judge. Indeed, a turn-by-turn analysis of these proceedings shows that conversational participants were not explicitly orienting to gender, but rather were orienting to the type of trial they were involved in (i.e., a sexual assault trial) and to the positioning of the accused as a possible agent of sexual acts of aggression. Nonetheless, orientations to or understandings of gender were made explicit in the judge's decision and thus seemed extremely significant to the outcome of the court's proceedings.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".