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Record W4317803613 · doi:10.1080/24694452.2022.2151406

A Pedagogy of Unbecoming for Geoscience Otherwise

2023· article· en· W4317803613 on OpenAlexaff
Christopher Reimer, Sarah-Louise Ruder, Michèle Koppes, Juanita Sundberg

Bibliographic record

VenueAnnals of the American Association of Geographers · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDisciplineSociologyPoliticsFlourishingEmbodied cognitionEpistemologyPosthumanEnvironmental ethicsPedagogyEngineering ethicsPolitical scienceSocial sciencePsychologyLawPhilosophyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.441
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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