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Science, Power, and System Dynamics: the Political Economy of Conservation Biology

2001· article· en· W2069528645 on OpenAlexaff
Samantha J. Song, R. Michael M’Gonigle

Bibliographic record

VenueConservation Biology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of VictoriaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsPoliticsFunction (biology)Conservation biologyPolitical scienceNatural resourceState (computer science)Resource (disambiguation)Conservation psychologyPower (physics)Action (physics)EcologyBiologyBiodiversityComputer science

Abstract

fetched live from OpenAlex

Abstract: Frustration with the lack of action on conservation issues by governments has sparked debate around the policy role of conservation biologists. We analyzed the political economy of conservation biology, that is, of the dynamics of the political and economic structures within which conservation biology operates, and we suggest more productive means for conservation biologists to achieve conservation goals. Within the modern state, conservation goals are marginalized because the growth needs of industrial capital have the highest priority. Environmental advocacy within this system largely addresses only proximate concerns and has limited success. Science is a product of modern society, but scientists now need to foster novel institutional arrangements in which humans can function within the limits of natural systems. This entails a larger recognition of the inherent contradictions residing within current institutions that themselves depend on unsustainably high resource flows. As one critical counterbalance to these institutions, we discuss community‐based management and research as primary institutions through which sustainable use of natural resources might be achieved.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.263
Teacher spread0.241 · 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.

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

Citations58
Published2001
Admission routes1
Has abstractyes

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