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Record W2979780420 · doi:10.1177/0308518x19877887

Two icebergs: Difference in feminist political economy

2019· article· en· W2979780420 on OpenAlexafffundabout
Rosemary‐Claire Collard, Jessica Dempsey

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCapitalismIcebergPoliticsScholarshipSociologyPolitical economyEconomyPolitical scienceNeoclassical economicsEconomicsGeologyOceanographyLaw

Abstract

fetched live from OpenAlex

In economic geography and beyond, a call for attention to difference or multiplicity – of logics, subjects, geographies – within capitalist and economic relations is often interpreted as a critique in the vein of JK Gibson-Graham: a call to explore capitalism’s alternatives, weaknesses – ‘cracks and fissures’. But there are feminist political economists for whom the multiplicity within and outside capitalism is a source of capitalism’s power; capitalism functions, accumulates and reproduces itself through heterogeneity. In this commentary, we focus on a particular underused theorist who exemplifies such an approach: Maria Mies. We put Mies in conversation with the much better-known Gibson-Graham via each of their depictions of economic relations as an iceberg. We consider each iceberg (and the understanding of capitalism they represent) in relation to capitalist natures scholarship in particular, drawing on our research on the production of emaciated caribou natures in Canada as a mini ‘field test’ for where the icebergs direct our analytical attention. We present these icebergs as a small step towards opening up a broader terrain of feminist theorisations of capitalism and difference than is sometimes recognised in economic geography and political ecology.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.355
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations32
Published2019
Admission routes3
Has abstractyes

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