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Priorities for protected area research

2018· article· en· W2808498921 on OpenAlexafffund
Nigel Dudley, Marc Hockings, Sue Stolton, Thora Amend, Ruchi Badola, Mariasole Bianco, Nakul Chettri, Carly N. Cook, Jon Day, Philip Dearden, Mary E. Edwards, Paul J. Ferraro, Wendy Foden, Roberto Gambino, Kevin J. Gaston, Natalie Hayward, Valerie Hickey, Jason Irving, Bruce Jeffries, A.P. Karapetyan, Marianne Kettunen, Lars Laestadius, Dan Laffoley, Dechen Lham, Gabriela Lichtenstein, John Makombo, Nina Marshall, Mélodie A. McGeoch, Dao Nguyen, Sandra Nogué, Midori Paxton, Madhu Rao, Russell Reichelt, Jorge Rivas, Dirk J. Roux, Claudia Rutte, Kate Schreckenberg, Andrej Sovinc, Svetlana Sutyrina, Agus Utomo, Daniel Vallauri, Pål Vedeld, Bas Verschuuren, John Waithaka, Stephen Woodley, Carina Wyborn, Yan Zhang

Bibliographic record

VenuePARKS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Victoria
FundersNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationParks CanadaClayoquot Biosphere TrustCentral European UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsGeographyPolitical science

Abstract

fetched live from OpenAlex

A hundred research priorities of critical importance to protected area management were identified by a targeted survey of conservation professionals; half researchers and half practitioners.Respondents were selected to represent a range of disciplines, every continent except Antarctica and roughly equal numbers of men and women.The results analysed thematically and grouped as potential research topics as by both practitioners and researchers.Priority research gaps reveal a high interest to demonstrate the role of protected areas within a broader discussion about sustainable futures and if and how protected areas can address a range of conservation and socio-economic challenges effectively.The paper lists the hundred priorities structured under broad headings of management, ecology, governance and social (including political and economic issues) and helps contribute to setting future research agendas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.084
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.013
Science and technology studies0.0070.005
Scholarly communication0.0180.015
Open science0.0040.011
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0270.005

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 head0.088
GPT teacher head0.308
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations22
Published2018
Admission routes2
Has abstractyes

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