Why Do We Need Social Theories? The case for science education research, feminist theories, and social justice
Bibliographic record
Abstract
Just like individuals who are assigned certain identities within society, culture, and institutions (e.g., men, women, teacher, student, English language learner), one can claim that disciplines such as science education have also their own identity (Weedon, 1996). And within those identities and assignments are inscribed practices and regimes of meaning that constitute, “ establish, [and extend] the parameters for how people are to inquire, organize, and understand [the] world ” (Popkewitz, 2001). Science education as a discipline resides geographically in a peculiar space that constitutes the “domain” of science fields and education field. However when the word theory is mentioned the dominant meaning usually invokes the understanding simply related to educational processes of access, such as teaching and learning. At least in the United States, often in science (and even math) education, the notion of theory is mainstreamed under the umbrella of what is known as theories of teaching and learning (Wilson & Peterson, 2006). The call for the special issue of JASTE on the importance of critical theories, once again invites us to re-think about the critical role of theory in science education.
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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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.158 |
| Scholarly communication | 0.021 | 0.054 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 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".