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Record W3136055947 · doi:10.1111/cobi.13732

Bridging research and practice in conservation

2021· review· en· W3136055947 on OpenAlexaff
Andrew N. Kadykalo, Rachel T. Buxton, Peter Morrison, Christine Anderson, H. Bickerton, Charles M. Francis, Adam C. Smith, Lenore Fahrig

Bibliographic record

VenueConservation Biology · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsParks CanadaMcGill UniversityThe North South InstituteEnvironment and Climate Change CanadaCarleton University
Fundersnot available
KeywordsIntermediaryBusinessEvidence-based managementEvidence-based practiceEmpirical evidenceBiodiversity conservationPublic relationsBridging (networking)Resource (disambiguation)Conservation psychologyFoundation (evidence)Knowledge managementEnvironmental resource managementEnvironmental planningMarketingPolitical scienceBiodiversityMedicineEcologyComputer scienceEconomicsAlternative medicineGeographyBiology

Abstract

fetched live from OpenAlex

Calls for biodiversity conservation practice to be more evidence based are growing, and we agree evidence use in conservation practice needs improvement. However, evidence-based conservation will not be realized without improved access to evidence. In medicine, unlike in conservation, a well-established and well-funded layer of intermediary individuals and organizations engage with medical practitioners, synthesize primary research relevant to decision making, and make evidence easily accessible. These intermediaries prepare targeted evidence summaries and distribute them to practitioners faced with time-sensitive and value-laden decisions. To be effective, these intermediaries, who we refer to as evidence bridges, should identify research topics based on the priorities of practitioners; synthesize evidence; prepare and distribute easy-to-find and easy-to-use evidence summaries; and develop and maintain networks of connections with researchers and practitioners. Based on a review of the literature regarding evidence intermediaries in conservation and environmental management, as well as an anonymous questionnaire searching for such organizations, we found few intermediaries that met all these criteria. Few evidence bridges that do exist are unable to reach most conservation practitioners, which include resource managers in government and industry, conservation organizations, and farmers and other private landowners. We argue that the lack of evidence bridges from research to practitioners contributes to evidence complacency and limits the use of evidence in conservation action. Nevertheless, several existing organizations help reduce the gap between evidence and practice and could serve as a foundation for building additional components of evidence bridges in conservation. Although evidence bridges need expertise in research and evidence synthesis, they also require expertise in identifying and communicating with the community of practitioners most in need of clear and concise syntheses of evidence. Article Impact Statement: Evidence-based conservation will not be realized without improved access to evidence. We call for intermediary evidence bridges.

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)0.356
metaresearch head score (Gemma)0.419
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.356
Threshold uncertainty score0.794

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3560.419
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0180.015
Science and technology studies0.0150.122
Scholarly communication0.0510.065
Open science0.0100.064
Research integrity0.0300.031
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 head0.325
GPT teacher head0.465
Teacher spread0.140 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations110
Published2021
Admission routes1
Has abstractyes

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