Influence of speaker's gender on speaker introductions at the 2018 <scp>ACVS</scp> Surgical Summit
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether formality of introduction differed between male vs female speakers at the 2018 American College of Veterinary Surgeons (ACVS) scientific meeting and identify other variables that predisposed introducers or chairs to informal introduction. STUDY DESIGN: Observational study. SAMPLE POPULATION: Thirteen session chairs introducing 68 lectures (41 by females, 27 by males) by 63 speakers. METHODS: Observers recorded the session introducer, speaker, and whether speakers were introduced with a formal or informal title. Information evaluated included type of oral presentation; introducer gender, year, and country of graduation from veterinary school; speaker gender; whether the speaker was a resident; and speaker's year of graduation. RESULTS: Female speakers were introduced by their first name in 9 of 41 introductions compared to in 1 of 27 introductions for male speakers. This difference reached statistical significance when data independence was assumed (P = .043); however, this significance was narrowly lost when data clustering on session introducer was controlled for (P = .067). CONCLUSION: In this study, female speakers were more likely than male speakers to be introduced by their first and last names rather than with their professional title at a recent ACVS scientific meeting. IMPACT: Additional research is required to determine the effect of this type of subordinate language and gender bias in veterinary surgery.
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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 | 0.004 | 0.020 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".