Women on the Academic Tenure Track: An Autoethnographic Inquiry
Bibliographic record
Abstract
For novice faculty on the tenure-track achieving tenure means job security [1]. However, despite idealistic expectations [2], novice faculty report unbalanced lives and feelings of loneliness, isolation, and rivalry between colleagues [3]. Novice faculty also report becoming dissatisfied, overworked, stressed, and physically ill [4] as they attempt to meet the often unwieldy, vague, and increasing tenure and promotion requirements [5]. Men and women experience the tenure path differently [6]. Research also suggests that the structure of the academic workplace and the duration of the tenure-track (usually six years, although the time period differs by institution; [1]) are designed in ways that discriminate against women [7]. For fear of looking less committed or serious than male colleagues, female faculty members may even refuse to take advantage of parental leave policies such as maternity leave, halting the tenure clock during a maternity leave, and may even reconsider having a child or delay parenting [8]. Tenure-track appointments commonly require novice academics to perform adequately in three different arenas: research, teaching, and service. However, the weighting of the components does differ [9]. Gender disparities (e.g., salary, merit pay, etc.) resulting from inequities escalate when universities place more emphasis on research relative to teaching and service [10]. Kawalilak and Groen speak to the challenges and tribulations encountered by novice faculty pursuing the academic tenure track and suggest that sharing experiences can be “therapeutic, healing, and affirming” [11, p.6] for novice academics. Thus, the purpose of this study was to share the story of four female academics on the tenure track..
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".