Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The conditions under which women academics work provide the impetus for this article. Current trends in feminist and other writing are moving us away from dwelling on the disadvantages women experience in the academy. Yet the findings from the two Canadian studies reported here suggest that issues around children and career, anxieties about evaluation, and fatigue and stress shape the daily lives of women academics. The women do find ways and means of coping and resisting, sometimes collectively, although one of the major responses—working harder and sleeping less—might be considered somewhat short of empowering. We also look at what the prospects are for changes in university policies and practices. Notes * Corresponding author: Department of Sociology and Equity Studies in Education, Ontario Institute for Studies in Education of the University of Toronto, 252 Bloor St West, Toronto, Ontario M5S 1V6, Canada. Email:sacker@oise.utoronto.ca Nevertheless, there are still recent publications on the topic coming out of other countries such as Australia and New Zealand (Brooks & Mackinnon, Citation2001; Luke, Citation2001) and the United States (Ropers‐Huilman, Citation2003). Journals examined included British Journal of Sociology of Education, Canadian Journal of Higher Education, Gender and Education, Resources for Feminist Research, Studies in Higher Education and Women's Studies International Forum. See the later section on 'Evaluation'. In 1999–2000, women comprised 12% of professors and 24% of senior lecturers and equivalent researchers in the UK (Times Higher Education Supplement, Citation2003); the closest equivalent in Canada would be full professors, of which women were 16% in 2001 (AUCC, Citation2002, p.21). It is difficult to compare countries because of different definitions of rank and different conventions in collecting statistics. Even within a country, statistics from different sources are not always consistent. We decided not to identify in the text which study each person participated in, as it would be tedious to have such repeated references. For those interested in sorting out which statements come from which project, here are the pseudonyms in the article grouped by project. Making a difference: Alicia, Bella, Beth, Georgina, Grace, Helen, Iris, Kaila, Kay, Lisa, Lucille, Mary, Moira, Nicole, Norene, Olivette, Rose, Ruth, Solange, Susan, Tamara, Terri, Wendy; Women academics blending private and public lives: Audrey, Brigid, Carol, Cynthia, Irene, Janice, Madeleine, Megan, Natalie, Paula, Rachel, Vanessa, Vivian. Typically the candidate submits all published work, a cv, a narrative of accomplishments and a teaching dossier (a record of student course evaluations, course outlines, teaching philosophy, etc.) Appraisals by internal and external reviewers of the scholarship as well as letters from former students are sought. A tenure committee evaluates the evidence and makes a recommendation to higher levels of management. Additional informationNotes on contributorsSandra Acker Footnote* * Corresponding author: Department of Sociology and Equity Studies in Education, Ontario Institute for Studies in Education of the University of Toronto, 252 Bloor St West, Toronto, Ontario M5S 1V6, Canada. Email:sacker@oise.utoronto.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it