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Multiple pathways to conservation success

2012· article· en· W1585639325 on OpenAlex
Corey C. Phillis, Sacha M. O’Regan, Stephanie Green, Jeanette E.B. Bruce, Sean C. Anderson, Jennifer N. Linton, Brett Favaro

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConservation Letters · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsVancouver Island UniversitySimon Fraser University
Fundersnot available
KeywordsOverexploitationProcess (computing)Environmental resource managementSoundnessAction planOrder (exchange)Environmental planningAction (physics)Conservation sciencePolitical scienceGeographyBusinessEcologyBiologyHabitatComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Conservation successes can and do happen, however, the process by which society achieves them remains unclear. Using a novel culturomics approach, we analyse word usage within digitized texts to assess the chronological order in which scientists, the public, and policymakers engage in the conservation process for three prominent conservation issues: acid rain in North America, global DDT contamination, and the overexploitation of African elephants for ivory. Variation in the order and magnitude of sector responses among the three issues emphasizes that there are multiple pathways to conservation success and that science is just one component. Our study highlights that while scientists can initiate the process, policy change does not occur in the absence of public interest. We suggest that the fate of conservation action is not solely determined by the scientific soundness of the conservation plan, but rather requires the engagement of scientists, public, and policy makers alike.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.996

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)0.0180.005

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 head0.046
GPT teacher head0.242
Teacher spread0.196 · 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