Threat Convergence: The New Academic Work, Bullying, Mobbing and Freedom
Bibliographic record
Abstract
The convergence of the casualization, fragmentation, intensification, segmentation, shifting and creep of academic work with the post-9/11 gentrificaton of criticism and dissent is a serious threat to academic freedom. Academic stress— manifested as burnout through amalgamation and creep of work, and as distress through bullying, mobbing and victimization— underwrites increases in leaves of absence. Non-tenure track faculty are hit particularly hard, indicating “contingency or the precariousness of their position” as relentless stressors. This is not exactly a SWOT analysis, where Strengths, Weaknesses, Opportunities and Threats are given due treatment. Rather, the focus is on this threat convergence as it resolves through historic displacements of the academic workplace and work. To what degree are the new policies for academic speech inscribed in academic work, regardless of where it’s done? As the academic workplace is increasingly displaced and distributed, are academic policies displaced and distributed as well? Observed at work, monitored at home and tracked in between—these are not so much choices as the cold reality of 21st century academic work.
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.067 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".