Navigating STEMification for critical geography educators: finding leverage in classroom and institutional pedagogies
Bibliographic record
Abstract
This paper grapples with the challenges posed to critical geography educators by STEMification, or the enshrinement of market-oriented forms of science and technology education as the normative ideal for education in general. In both reactionary and progressive contexts, STEMification decontextualizes scientific and technological activity and deepens existing hierarchies of knowledge based on quantification, perceived scientific rigour, commercialisation, and employability. Critical geographical knowledges often incur misrecognition, dismissal, and in some cases, outright prohibition under such conditions. Offering strategies for navigating and contesting STEMification, this paper draws on collective auto-methods, analysing narrative vignettes from our pedagogical practices as critical geography educators. We offer the notion of seeking leverage in the face of STEMification: protecting ourselves and seeking traction within our institutions by translating our goals into familiar or sanctioned forms, while using those forms to alternative ends. To that end, we highlight seven pedagogical strategies: (1) meeting students where they are, (2) using applied examples, (3) grappling with the limits of problem-based learning, (4) disalienating students from assessment, (5) integrating critique with alternatives, (6) anticipating both resistance to and desire for critical content from students and colleagues, and (7) recognising the limits of institutional environments.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.005 |
| 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".