Bibliographic record
Abstract
The COVID-19 pandemic has demonstrated that although learning can and sometimes does occur without teaching, on any significant scale, and especially among the most marginalized and vulnerable children, a lot of learning does not occur when children are deprived of teachers and teaching. Any questions of learning loss in the short term and learning transformations in the long run cannot therefore be addressed in any meaningful way without examining the short- and longer-term impacts of the pandemic on losses, gains, and transformations in teachers and teaching. This article analyzes actual and likely pandemic consequences of and insights deriving from remote access, digitally based interactions, and physical distancing in relation to three core characteristics of teaching and teacher quality. These are the development of “teacher expertise”, the nature of teaching as an “emotional practice” in which the well-being of students and teachers is reciprocally interrelated, and the ways in which external changes either enrich or deplete teacher’s “professional capital”, especially their “social capital”. Beyond post-pandemic narratives of educational doom on the one hand and of jubilant celebrations of bright spots and silver linings on the other, the article concludes that the future of teaching after COVID-19 will actually be complex, uncertain, and contingent on the policy decisions and professional directions that are set out in the recommendations to this report.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".