Philosophy of education in a new key: Who remembers Greta Thunberg? Education and environment after the coronavirus
Bibliographic record
Abstract
This paper explores relationships between environment and education after the Covid-19 pandemic through the lens of philosophy of education in a new key developed by Michael Peters and the Philosophy of Education Society of Australasia (PESA). The paper is collectively written by 15 authors who responded to the question: Who remembers Greta Thunberg? Their answers are classified into four main themes and corresponding sections. The first section, ‘As we bake the earth, let's try and bake it from scratch’, gathers wider philosophical considerations about the intersection between environment, education, and the pandemic. The second section, ‘Bump in the road or a catalyst for structural change?’, looks more closely into issues pertaining to education. The third section, ‘If you choose to fail us, we will never forgive you’, focuses to Greta Thunberg’s messages and their responses. The last section, ‘Towards a new (educational) normal’, explores future scenarios and develops recommendations for critical emancipatory action. The concluding part brings these insights together, showing that resulting synergy between the answers offers much more then the sum of articles’ parts. With its ethos of collectivity, interconnectedness, and solidarity, philosophy of education in a new key is a crucial tool for development of post-pandemic (philosophy of) education.
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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 | 0.010 | 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 | 0.006 | 0.028 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".