How to Choose an English Doctoral Program: A Guide for Women, First-Generation University Students, and Their Mentors
Bibliographic record
Abstract
Featuring data from a nationwide survey of English faculty in Canada, this study focuses on the reasons that faculty offered for choosing their respective doctoral programs. This analysis pays particular attention to gendered disparities in participants’ responses and to disparities associated with parental educational level, two variables that sometimes intersect in striking ways. This investigation explains why some reasons that women and first-generation university students provided for their choice of doctoral program can increase a student’s chances of entering a poorly ranked program. Employing previously unprocessed data from Statistics Canada, this article also presents information to help would-be PhD students who are women, first-generation university students, or both make more informed decisions about where to pursue doctoral study to be more competitive in the academic job market if they aspire to be professors. This article is simultaneously designed to increase faculty mentors’ understanding of the problematic logic that women and first-generation university students may use when selecting their PhD programs so that such mentors can better address these students’ misperceptions about doctoral studies and the profession.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".