Science education for growing networks of critique and altruism: striving for increased social justice and environmental vitality
Bibliographic record
Abstract
In the 1998 policy document, Beyond 2000, authors suggested that school science tends to prioritise education of few potential scientists. Apparently, little has changed since then. Given multiple apparent crises, however, like the climate emergency, it seems imperative that school science (or ‘STEM’) education be dramatically transformed to prioritise social justice and environmental vitality (‘ecojustice’). This, however, seems challenging. School systems appear enmeshed in tightly-woven networks of living, nonliving and symbolic actants apparently generally collaborating to maximise elites’ profits while compromising wellbeing of most other (a)biotic things. After elaborations of claims like those above, however, a science/STEM education framework that aims to help develop cultures of ‘critical altruism’ promoting increases in ‘ecojustice’ is described and critically defended. Some elements of this programme may be useful – albeit with caveats – to curriculum developers with similar goals. It prioritises direct instruction about apparently problematic relationships among fields of STEM and societies and environments and preparation of students for implementing their well-researched actions to help overcome issues in such relationships concerning them. Although this pedagogy seems to have had successes, it also appears that much work is needed to help spread values inherent to it across larger, perhaps more global, material-semiotic networks.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".