Bibliographic record
Abstract
Dear Sir, I thank Dr. Damant for her letter and will attempt to address some of the points that have been raised. First, I would like emphasize that the President's Message does not represent the views of the CVMA on gender issues in veterinary medicine. I developed the President's Message after reviewing the body of veterinary literature on this topic. I agree that incomes of men and women in the veterinary profession are less than optimal, and women are faring less well than men in this regard. A 2000 CVMA National Survey of Graduates (1997, 1998, 1999 Canadian veterinary graduates) revealed that the average annual salary of female graduates working an average of 1980 h/y was $45 000 (22.22/h) and that of male graduates at 2055 h/y was $50 000 ($24.46/h). The trend in compensation differences by gender persisted by practice type and by seniority ( 2 y out), excepting for associates 2 y postgraduation. Similarly, the Brakke Management and Behavior Study (1) conducted in the United States in 1998 revealed that women with the same ownership status, years of experience, and hours worked earned dramatically less than their male counterparts. I disagree that feminization is occurring to a lesser degree in the veterinary profession than in human medicine and dentistry. Women comprise 50% to 60% of medical students in the United States and Canada today and 50% of students in most Canadian dental schools (2,3). In contrast, many veterinary colleges (including some in Canada) report that close to 80% of their student population is female. I agree that men, on average, may have lower academic grades than women. However, in my experience and in that of other associate deans across North America, men compete well when they choose to apply for admission to veterinary colleges. The proportions of men and women in the applicant and admitted pools are almost identical, but men are not applying to veterinary colleges in the same proportions. I did not state that men are not caring or nurturing, neither did I categorically state that the caring and nurturing aspects of the profession have been enhanced by the increased participation of women in the profession. My statement reads “...may have been enhanced.” The CVMA has been devoting considerable resources and energy towards improving the economic well being of the veterinary profession in Canada through its National Benchmarking Program, as part of its priority, The Successful Practice of Veterinary Medicine.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".