Promoting Students’ Critical and Active Engagement in Socio-Scientific Problems: Inter-Trans-National Perspectives
Bibliographic record
Abstract
There are many potential harms to individuals, societies and environments associated with powerful networks of living, nonliving and symbolic entities (actants), such as financiers, banks, think tanks, transnational trade organizations and agreements, competitiveness, scientists, engineers, universities, governments, military, advertisements, entertainment, etc. Among myriad harms, perhaps the most serious is devastation from climate change linked to fossil fuel uses. Given apparent roles of many governments in supporting powerful problematic networks that involve fields of science and technology, many scholars recommend that school science not only enlighten students about harms and encourage them to make logical personal decisions about associated controversies but also prepare them to take socio-political actions that might contribute to their conceptions of a better world. In this chapter, international science education scholars discuss their uses and analyses of the ‘STEPWISE’ curricular and pedagogical framework—which is intended to facilitate such critical and activist science education. After a theoretical defence of the framework, a description is provided of a teacher’s 3-year efforts to use it in his secondary school science teaching. This is followed by five summaries of theoretical analyses of the framework by scholars from five countries (Australia, Brazil, Canada, the UK and the USA), including in terms of discursive psychology, neoliberalism, critical discourse analyses of well-being, inquiry-based learning, professional development and network mobilization in informal (online gaming) and formal school science and teacher education contexts. The chapter concludes with a brief summary of some relative merits of the STEPWISE framework and with a call for continued critical reflective practice.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.015 |
| 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".