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Record W1999658321 · doi:10.1017/s1537592706220140

Denaturalizing Ecological Politics: Alienation from Nature from Rousseau to the Frankfurt School and Beyond

2006· article· en· W1999658321 on OpenAlexaboutno aff
Kerry H. Whiteside

Bibliographic record

VenuePerspectives on Politics · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsAlienationSociologyMeaning (existential)EpistemologyArgument (complex analysis)Social scienceEnvironmental ethicsPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

Denaturalizing Ecological Politics: Alienation from Nature from Rousseau to the Frankfurt School and Beyond. By Andrew Biro. Toronto: University of Toronto Press, 2005. 270p. $60.00 cloth, $24.95 paper. Andrew Biro's dense argument for a “denaturalized ecological politics” should have wide appeal. At one level, it should make political theorists generally—whether or not they consider themselves “ecological theorists”—reflect more systematically on how concepts of nature structure the ideas of canonical thinkers. Political ecology is too often treated like a specialty shop in the theory emporium, a boutique that one enters or not according to the inclinations of taste. In fact, its insights recast the central concerns of political theory broadly conceived. Just as feminists have uncovered how gendered concepts are woven throughout the entire fabric of political discourse, so ecological political theorists demonstrate how nature in multiple guises (wildness, savagery, emotional connectedness, fecundity, scarcity, etc.) subtly inflects the meaning of notions of rights, justice, and human well-being. In this regard, Biro's perceptive analyses of Rousseau and Marx—like John Meyer's reading of Aristotle and Hobbes in Political Nature (2001)—add heft to a growing literature that, in the name of environmental concern, wrings new meaning from familiar theorists.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.758

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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 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

Citations8
Published2006
Admission routes1
Has abstractyes

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