How Vulnerable Am I? An Experiential Discussion of Tenure Rhetoric for New Faculty
Bibliographic record
Abstract
Attaining tenure is a significant stress and source of motivation for tenure-track new faculty. A process that is misconstrued. misrepresented, and rife with rumour. To make matters worse, the process varies with institution, candidate, and year. This article uses a narrative-auto-ethnographic approach to unpack the concerns and questions about tenure that I, as a tenuretrack new faculty member, have experienced. The literature is used to contextualize these issues within the experiences of other new faculty reported in the research - seeking the truth behind the stories. The article concludes with focused reflections on White's (2008) list of strategies to help navigate the complexities of the tenure process, as well as recommendations for how to approach the tenure-track process.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.038 | 0.039 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".