A Pedagogy of Unbecoming for Geoscience Otherwise
Bibliographic record
Abstract
White supremacy and human exceptionalism are the epistemological and political foundations of contemporary geosciences. Disciplinary norms and ways of being call forth the geoscientist as “man of reason.” How do we, as educators, invite students to analyze and act on the interconnected political ecological challenges of the current environmental crisis without reinforcing the man of reason, now refashioned as the reformed and greener “ecosystem man-ager”? What do we need to unlearn, to unbecome? Where and how can we do this unlearning and unbecoming? This article positions pedagogy as a site of disciplinary and institutional transformation. We outline an antiracist, anticolonial pedagogical framework—what we call a pedagogy of unbecoming—that nurtures an extrarational, embodied, and relational geosciences otherwise. We share our experience, as white settler educators in persistently white disciplines, of enacting this pedagogy of unbecoming and outline specific protocols we used in course design. In the end, our efforts to transform the look and feel of geographic knowing are pragmatic attempts to walk alongside endeavors led by marginalized communities—inside and outside of academia—to build worlds otherwise. We invite peers to join in an ongoing process of unbecoming to build the ontological and epistemological conditions necessary for mutual flourishing.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".