Designing Frameworks for Authentic Equity in Science Teaching and Learning: Informal Learning Environments and Teacher Education for STEM
Bibliographic record
Abstract
Abstract In order to advance authentic equity in science education, it is salient to have frameworks that allow educators and researchers to design learning environments, activities, and research agendas that centers students’ strengths in order for them to achieve full participation in science. As such it is important to consider the social identities of science education stakeholders—teachers and students—in teacher education. However, as identity is complex, it requires research approaches that elucidate not only the nuances of teacher identity but also the complexities of science teaching and learning environments. This article describes a collaborative research project that aimed to unpack the relationship between teacher identity and learning to teach. It outlines the collaborative process of theory building that includes teacher participants and the research team and how the framework for teacher education emerged that considers the various aspects of designing equitable and liberatory science learning.
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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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".