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Record W2790924661 · doi:10.1111/anti.12389

Disaster Capitalism and the Quick, Quick, Slow Unravelling of Animal Life

2018· article· en· W2790924661 on OpenAlexaff
Rosemary‐Claire Collard

Bibliographic record

VenueAntipode · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser University
FundersU.S. Bureau of Land ManagementSyracuse University
KeywordsCapitalismColonialismState (computer science)TemporalityRelation (database)PoliticsPolitical economySociologyPolitical scienceLawPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract Sea otters have barely survived centuries of colonial and capitalist development. To understand why, I examine how they have been oriented in capitalist social relations in Alaska, and with what effects. I follow sea otters through three overlapping political economic episodes, each of which shapes the next: colonial expansion and the fur trade; petro‐capitalism and the negligent neoliberal state, culminating in the 1989Exxon Valdezoil spill; and finally, spill cleanup and “green” capitalism, when sea otters are produced as data points and spectacle. In each episode, I describe (1) sea otters’ orientation in relation to capitalism and the state, and (2) the nature and temporality of violence and ecological loss that attends their orientation. In conversation with theorisations of extinction as a “slow unravelling”, I suggest animal life can unravel less slowly than haltingly—quick, quick, slow—and that the unravelling and animals’ orientation in capitalism are co‐constituted.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.034
Scholarly communication0.0040.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.345
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations30
Published2018
Admission routes1
Has abstractyes

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