Currents in STSE education: Mapping a complex field, 40 years on
Bibliographic record
Abstract
Abstract It has been 40 years since science, technology, society, and environment (STSE) education first appeared in science education research and practice. Although supported among many educators worldwide, there is much confusion surrounding the STSE slogan. Widely differing discourses on STSE education and diverse ways of practicing, have led to an array of distinct pedagogical approaches, programs, and methods. We are left wondering how we might orient ourselves amid such a diversity of propositions. What does STSE look like in practice? What ideological orientations underpin its practice? In this paper, we review the research literature and educational practices in STSE education to (1) map out a typology of STSE education in the form of currents and (2) provide a heuristic that educators can use for critical analysis of discourses and practices in the field. We identify, explore, and critique six currents in STSE education: application/design, historical, logical reasoning, value‐centered, sociocultural, and socio‐ecojustice currents. We suggest that these currents may serve as a didactic tool for others, a framework that will assist educators in informing their own theoretical understandings, choices, and practices in the context of STSE education. © 2011 Wiley Periodicals, Inc. Sci Ed 95:601–626, 2011
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".