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Record W2751873692 · doi:10.1177/2043820617717847

Rethinking the subject, reimagining worlds

2017· article· en· W2751873692 on OpenAlexaff
Susan Ruddick

Bibliographic record

VenueDialogues in Human Geography · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEpistemologyFraming (construction)SociologyScholarshipSubject (documents)Sign (mathematics)AmbivalenceEnvironmental ethicsPhilosophySocial psychologyPsychologyPolitical scienceLawHistory

Abstract

fetched live from OpenAlex

The ecological crisis is also an ontological crisis. It raises questions about our ethical response-ability to this world, calling for a rethinking of the human–nature divide. Vitalist approaches and scholarship on the affective turn have shifted our understanding of our relations to nonhuman others, but they remain constrained: limited to proximate attachments; ambivalent or agnostic in the face of conflict; unable to move beyond the celebration of a lively earth. At issue I feel is a methodological individualism that haunts these offerings when confronted with questions of the ethical composition of a larger whole. Building upon Sharp’s invitation to explore ‘our continuity with nonhuman agencies’, I investigate the ethical basis for a reimagined subject in a series of becomings: the becoming nature of God, becoming animal of man, and becoming sign of earth. Drawing on the writings of Spinoza, Deleuze and Guattari, and Peirce, I rework this familiar terrain on two counts. First, I examine how the content of each becoming invokes distinct relational dynamics and complicates the ‘problem of composition’. Second, I draw on Spinoza’s differentiated concept of power (as potentia and potestas) and the concept of the composite individual to suggest an alternative way of framing our collaborations with the nonhuman world.

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)0.013
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.128
Scholarly communication0.0210.035
Open science0.0020.012
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.059
GPT teacher head0.348
Teacher spread0.289 · 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 designTheoretical or conceptual
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

Citations69
Published2017
Admission routes1
Has abstractyes

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