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Record W2978802813 · doi:10.1177/2514848619878167

Visioning African lionscapes: Securing space, mobilizing capital, and fostering subjects

2019· article· en· W2978802813 on OpenAlexaff
Sandra McCubbin, Alice J. Hovorka

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsSummitCorporate governanceGovernment (linguistics)SociologyPoliticsSubjectivityHonorCapital (architecture)Political sciencePublic administrationLawManagementEconomicsHistoryGeographyEpistemology

Abstract

fetched live from OpenAlex

In September 2016, 14 months after the illegal killing of Cecil the lion raised an international furore over trophy hunting, 58 individuals gathered at Oxford University for the Cecil Summit, a meeting of experts designed to vision the future of lion conservation in honor of Cecil. This paper explores the Cecil Summit through an analytic of government as a means to provide new insights into securitized and neoliberal conservation governance in action. On this basis, we show how the actors emboldened by the Cecil Moment claimed the authority to vision the Cecil Movement. Using video and document review, and semi-structured interviews, our discourse analysis highlights three components of intervention into African lionscapes emerging from the summit—securing space, mobilizing capital, and producing subjects—that are founded upon claims to scientific and economic rationality as well as specific representations of lions and rural Africans. Our analysis of the vision contributes to recent discussions in political ecology about the dovetailing of conservation, security, the economy—and we add—subjectivity. We conclude by pointing to the way in which militarized conservation appears to be inching closer to the lion and offering a critique of the vision for lion conservation put forward at the Cecil Summit.

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 score0.375
Threshold uncertainty score0.572

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 studies0.0010.000
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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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