Understanding Tension-Filled Tenure Track Stories: Currere, Autobiographical Scholarship, and Photography
Bibliographic record
Abstract
In this paper I utilized the currere method and my experiences as a tenure track hire. Currere provided a framework that allowed me to remember and then engage my ways of knowing and immerse myself in supportive contexts. Specifically, I was able to deepen my understandings, learn, imagine up, and over time shift my tenure track stories. The complex, sometimes hegemonic institutional narratives embedded along my tenure track, regularly resulted in tension. In response to the tension and because of my enactment of the currere, I was able to remember and reflect on what I know and value, think about who I am and who I am becoming, including who I want to be as a professor. This work includes photographs because once I gave myself permission to play, taking, viewing, and manipulating pictures became part of my shifting tenure track identity stories.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".