Critical Imagination for Transformative Agency: Pedagogies for Science Teacher Education
Bibliographic record
Abstract
ABSTRACT This paper theorizes transformative agency and its potential to promote justice‐oriented science teacher education. We argue that science education often acts as a disimagination machine, constraining possibilities for envisioning and enacting transformative change. To contest this reality, we draw on critical perspectives in science education, specifically Paulo Freire's and Simone Weil's philosophies to theorize transformative agency as encompassing three dimensions: a) reading the world to identify injustices, b) imagining untested feasibilities, and c) writing the world anew. In doing so, we act upon the belief that inherited practices of science education that negate collective joy must be challenged. We expand current conceptualizations of transformative agency by proposing critical imagination as one of its core components, enabling the envisioning of possibilities for change. We propose three pedagogical approaches for cultivating critical imagination: a) facilitating practices that move beyond the self to recognize multiple human and nonhuman others; b) adopting a planet‐centred orientation to education transcending human‐centered approaches; and c) troubling dominant spatial and temporal scales of thinking. We argue for the need to develop liberatory pedagogies that bring critical scientific questions to justice issues while nurturing critical imagination. This entails conceiving agency as more than responsive classroom practices but rather as achieving justice‐oriented commitments, agendas and visions that center the world and its necessities.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".