Science and Technology Education for Critical and Active Civic Engagement: Network Possibilities
Bibliographic record
Abstract
Although there is much to celebrate about science and technology, including improvements to human longevity from medical technosciences, there also are many associated harms. Perhaps most worrisome are threats from climate change, but manufactured foods, nuclear power, electronic surveillance systems, etc. also appear problematic. While culpability for such harms is complex, financiers and corporations, etc. seem to have funneled wealth towards themselves at expense of biotic and abiotic environments. Since many governments appear to be enmeshed in such power networks, it seems clear that science education must help educate students about possible causes of and actions to address technosciences-related harms. In this symposium, four papers describe different aspects of the ‘JustAct’ project – which has been using action research to learn about educators’ efforts to encourage and enable student-led critical and active civic engagement (CACE). In light of the dispositif concept, the four sub-projects investigate possibilities for and factors affecting CACE in very different contexts — including: suburban high schools, a community college and community action groups. Findings indicate some mobilization of CACS, and they suggest some factors that – while acknowledging and valuing the rhizome metaphor – may contribute to this overall project.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".