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Record W2281869121 · doi:10.1177/0308518x15608079

Fixing biodiversity loss

2015· article· en· W2281869121 on OpenAlexaff
Jessica Dempsey

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsUniversity of Victoria
FundersSmithsonian Institution
KeywordsAnthropoceneBiodiversityEnvironmental ethicsPlanetary boundariesScarcitySustainabilityPolitical scienceSovereigntyEnvironmental resource managementSociologyEconomicsEcologySustainable developmentLawPolitics

Abstract

fetched live from OpenAlex

In the midst of the “sixth extinction” and declarations of the so-called Anthropocene, scientists and conservationists are debating the nature of planetary limits. They are also rethinking the very goal of nature conservation in a postnatural direction that is less oriented on saving pristine nature. To shed light on this contemporary debate, this paper looks back to examine three “circuits of power and knowledge” where biodiversity loss is constituted as an ecological crisis with humanity in its crosshairs and later as a more flexible problem of trade-offs. The paper contributes a grounded, empirical examination of the production of, and changing nature of “global biodiversity limits,” showing how they emerge through articulations between power laden and elite ecological-economic knowledge and frameworks, global biopolitical and ethical concerns, state-capital accumulative logics, and national security interests. Reaffirming a critical stance on limits and tracing a persistent ontology of scarcity in global biodiversity science and policy, the paper draws from the story of global biodiversity limits to inform the current discussion of the postnatural turn.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.576

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 head0.030
GPT teacher head0.204
Teacher spread0.174 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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