Doing Academia in “COVID-19 Times”
Bibliographic record
Abstract
Whenlockdownsbegan, timebegan to takeon a quite different quality. For manyof us, days were no longer ordered according to our usualschedules: school drop off, making the train, meetings, teaching, scheduling tomorrowandnext week, school pick up, social activity, children’s after school activities, and bedtime routines. For some, keeping a routine is something we now have to make an effort to do(though“routine”might still be too generous a term forwhat our lives have looked like).Other than some scheduled Zoom meetings to focus our attention, getting up, putting together three meals a day, an hour of mandated “exercise”outside, and wine o’clock arewhathave keptus ticking. It sounds simpler, in a way;relaxed, even,compared to the relentlessness of our ordinary schedules. But it is not. It is not relaxing. It is not ordinary.The lockdown, the shutdown, is more than a rupture of the ordinary. It seems we are experiencinga profound, traumatic break. The conditions of the ordinary, as Jonathan Crary has argued, are “defined by a principle of continuous functioning”(Crary, 2014, p. 8).More specifically, continuous productive functioning. Just functioning isn’t enough.And as we now start to face what ‘the new academic year’ looks like, the profundity of what we have experienced seems to be second to the need to ‘get back to work’ and maintain our willingness to invest in the competitive practices that fuel the systems we live and work in.Our response to this emerged from making similar observations from our respective contexts (New Zealand, Canada, the United Kingdom,andBelgium), and is shaped by our work in the field of educational philosophy. Originally published as a blog, this short essay is an attemptto make sense of the way in which academia has responded to these profound changes in how we live and work,toask what it says of academia that having a say on these matters, matters, and to question the will to provide answers amidst ongoing turmoil.(And, yes, we also acknowledge the ironyofour having something to say about people having something to say about COVID-19).
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.155 | 0.059 |
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".