Student-teachers’ Inquiry-based Actions to Address Socioscientifc Issues
Bibliographic record
Abstract
We are facing many challenges associated with fields of science and technology. Arguably of most concern is Climate Change, but there are many other issues, such as food quality, distribution and safety. Many of these problems may be related to individuals’ tendencies towards repeating cycles of perhaps irresponsible consumption of goods and services — apparently largely under the influence of laissez faire capitalism. Progress has been made in addressing such issues by, for example, encouraging students to consider complex socioscientific issues, take positions about them, and develop plans of action to address them. In the study reported here, we explored effects on student-teachers’ likelihood of implementing inquiry-based activism projects in their future teaching by requiring them to conduct such projects in the context of a university-based science teacher education course. Of the ten student-teachers we studied most closely, four of them appeared to be highly likely and another three of them appeared to be moderately likely to implement inquiry-based activism projects in their future teaching. Based on constant comparative analyses of qualitative data, factors that seemed to influence student-teachers’ likelihood of implementing such projects in their future teaching included their: i) self-directed research (as part of the course described above), ii) prior experiences relevant to WISE issues and activism, iii) views about the nature of science and technology (NoST), and iv) orientation towards Products Education. Based on these findings, recommendations for science teacher education are provided that might, eventually, increase the wellbeing of individuals, societies and environments; and, in concert, better manage societal production and consumption practices.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".