Bibliographic record
Abstract
Humanity and much else on earth appear to be facing existential crises, like the climate emergency, and ongoing problems like cancer that may interact with other crises and create much worse polycrises. Although fields of science, technology, engineering and mathematics (STEM) are involved in many such crises, many analysts suggest that ultimate blame—while invariably uncertain—should be mainly directed at capitalists. It is apparent, that financiers and corporations have been highly successful at assembling massive ‘teams’ (‘dispositifs’) of supporters—including numerous other living (e.g., politicians and STEM workers), nonliving (e.g., massive extraction machines) and symbolic (e.g., ‘efficiency’) entities into extensive and deep assemblages promoting values like competitiveness, individualism and costs externalisations. Their complexity seems to make them highly resistant to change. In this article, a pedagogical schema is described and defended (with examples) that may help generate more citizens willing and able to critique relationships among STEM and other societal members and environments (STEM-SE) and independently develop and implement well-researched and negotiated powerful actions to overcome STEM-SE harms of their concern. Among many factors affecting the schema’s successes, it seemed very helpful that the local curriculum was congruent, the teacher had more holistic and critical views about science, such as regarding its economic relations, and because the teacher agreed to directly teach students, with application activities, several possibly problematic STEM-SE relationships and sample possibly rectifying actions. STEM education schema like that, however, only seem broadly feasible with concerted community efforts to build more global ecojustice dispositifs.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".