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Record W2467331887 · doi:10.1002/wsb.674

What makes wildlife wild? How identity may shape the public trust versus wildlife privatization debate

2016· article· en· W2467331887 on OpenAlexaboutno aff
Markus J. Peterson, M. Nils Peterson, Tarla Rai Peterson

Bibliographic record

VenueWildlife Society Bulletin · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersUniversity of Texas at El PasoNational Institutes of HealthNorth Carolina State University
KeywordsWildlifeWildnessPublic trust doctrineIdentity (music)Argument (complex analysis)Wildlife managementEnvironmental ethicsWildlife conservationConstruct (python library)DoctrinePolitical scienceSociologyLaw and economicsLawEcologyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Wildlife conservation policy discussions in the United States and Canada often revolve around historical accounts of the success of wildlife management grounded in the public trust doctrine. We suggest that the usefulness of these discussions is partially limited by failure to consider the importance of wildlife “identity” rooted in freedom (i.e., how humans socially construct the “wildness” dimension of wild animals). To demonstrate the interrelations between identity and freedom, we explain that the class of subjects people care most about—partners, children, and people in general—typically should not be privately owned (i.e., chattel) because freedom (as opposed to slavery) is generally accepted as central to human identity, and its abrogation therefore degrades human identity. The degree to which this ethical argument applies to privatization of wildlife depends upon the relationship between freedom and the identity of wildlife as perceived by society. Thus, we suggest policy decisions regarding privatization of wildlife will be more accurately deliberated if society and wildlife professionals more completely considered the degree to which freedom is essential to a wild species’ identity and the degree to which that identity is inviolable. © 2016 The Wildlife Society.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.004
Open science0.0020.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations10
Published2016
Admission routes1
Has abstractyes

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