Teaching in the Age of Covid-19—1 Year Later
Bibliographic record
Abstract
In March 2020 I published the 'emergency editorial' in Postdigital Science and Education and invited the community to 'explore all imaginable aspects of this large social experiment that the Covid-19 pandemic has lain down in front of us' (Jandri 2020a: 237). Articles immediately started pouring in; within weeks, the journal's contributions had been recognized by institutions such as the World Health Organization, the US National Library of Medicine's Nature Public Health Emergency Collection, and UNESCO (see Jandri 2021 for details). After publication of the October 2020 issue of Postdigital Science and Education, 1 consisting of almost 60 articles on the Covid-19 pandemic, the first wave of pandemic research has wound down. As it has become obvious that Covid-19 is here to stay, research on immediate Covid-19 experiences and responses slowly gives way to research which 'reaches beyond the pandemic to the point where the pandemic experience is transformed from an object of research to an intrinsic part of our theories, approaches, research methodologies, and social struggles' (Jandri 2021: 262).
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".