Bibliographic record
Abstract
This study extends the literature on gender, language, and the professions by examining the language practices of two participants from the communities of practice (Eckert & McConnell-Ginet 1992; Lave & Wenger 1991) of physicians in emergency medicine departments in the French-speaking province of Quebec, where the percentage of women who are becoming medical doctors is the highest it has ever been – higher than in other Canadian provinces, higher than the national average, and still growing (Kondro 2007). I argue that the growing number of women in this male dominated profession strongly depends on their success in adapting themselves and their lifestyles to an institutional structure that accommodates men and the lifestyles of men. The following study analyzes transcript data from two interviews with a male and a female physician. The research questions being pursued in the analysis are the following: How do these physicians discursively construct and negotiate their professional identity(ies)? What roles do gender and status play in their negotiation of these identities? The findings show that participants’ use of positioning (Davies & Harré 1990; Eckert & McConnell-Ginet 2003) and narrative contribute to the construction of this community of practice as relatively masculine and may point to one of the reasons women continue to be underrepresented in this profession.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".