Enhancing Women's Graduate Education: Workshopping Women's Socialization to the Academic Profession
Bibliographic record
Abstract
This article outlines the impact of a professional development workshop, Women in Academe, arguing that it offers a first step in addressing a crucial gap in graduate students' education.Workshop participants' feedback confirms the usefulness of providing opportunities for discussion and strategizing to enhance women's career success in the academy.Résumé Cet article souligne l'impact d'un atelier de perfectionnement professionnel, "W omen in Academe," qui soutient qu'il offre un premier pas vers la résolution de l'écart crucial entre l'éducation des étudiantes du deuxième et du troisième cycle.La rétroaction des participantes à l'atelier confirme qu'il était utile d'offrir des occasions pour la discussion et de faire le stratagème pour promouvoir le succès de carrière des femmes dans l'académie. Sharing StoriesDuring my graduate school career I held a teaching assistant position for two years in a second-year humanities course that explored the connection between the sciences and the humanities.The course invited students to study the lives, work and cultural context of two modern scientists, Charles Darwin and Albert Einstein.The instructor for the course had devised an introductory lecture that encouraged students to understand, from the very first class, just how deeply the lives and work of these two scientists have pervaded many aspects of western culture.During the course of the lecture, he intermittently removed t-shirt after t-shirt with logos that had some connection to either Darwin or Einstein.He had an extensive collection of these shirts.At first, students didn't notice what he was doing, assuming he was just removing a shirt because he was warm.However, as the lecture progressed, the lecture hall would fill with an energized buzz as the students realized he was making a point -and they were getting it -Darwin and Einstein's science pervades our current lives.I felt exhilarated the first time I saw this lecture gimmick that captured students' attention and interest, and made an important point that they would carry with them throughout the year-long course.As the lecture progressed, however, my own exhilaration turned to concern and I wondered to myself, "Could I have performed this lecture?Could I, a young, academic woman, give a lecture as I removed successive shirts, after shirt -strip off my clothes -and still be seen to be making a valid academic argument and be taken seriously?"I wasn't sure.Recently, upon recounting this story to a
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".