Multinational Policy Analyses: Third Time Around
Bibliographic record
Abstract
This chapter is a multinational policy analysis focusing on what happened in the aftermath of the Covid-19 pandemic in Brazil, Canada, Portugal, and USA. It is a follow-up to the first two analyses which were also conducted collaboratively (2019, 2022). The studies are constant-comparative. The four-country approach illuminates policies and practices in what hopefully is post-Covid-19 times. Neoliberal approaches to policymaking and education in general ensure that the technicalities of teaching received heightened attention to the neglect of the well-being of teachers and the agency afforded them. The critical situation of the teaching profession in the post-pandemic time means there are teacher shortages as well as the lowering of working conditions for teachers. Turmoil and crisis are two words that describe the education sector and are clearly illustrated in the media and in research. While the need to invest in education, and particularly teachers' education and career prospects, is reiterated in policy discourse, it is far from being a reality as the four cases show. The pandemic has exacerbated the existing problems in the field of education, causing heightening concern about teachers' recruitment, working conditions and well-being.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".