Where will we put you? Stories of Office Space from a Precarious Academic in Higher Education
Bibliographic record
Abstract
In the fall of 2021, faculty were preparing for a return to campus after 18 long months of teaching their courses from home. In a post-pandemic world, as we get back to normal in society, many precarious faculty are wondering what “normal” they’re returning to. This paper draws on the author’s personal experience with office space on campus as a precarious faculty member. First, this paper will review the literature on office space for precarious faculty, noting specifically the importance of office space for newcomers to the organization. Secondly, this paper will offer a series of personal reflections of being given office space as a precarious faculty member at four institutions between the years of 2016 and 2018. Finally, this paper will compare the personal reflections with that of the literature and offer a discussion of the inconsistencies of resources that precarious faculty are allocated and the consequences that arise.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.030 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".