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Record W1513258353

The conservation social sciences: What?, how? and why?: a report for conservation organizations, foundations, practitioners, agencies and researchers

2015· article· en· W1513258353 on OpenAlexfundno aff
Nathan Bennett

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPublic relationsSociologyPolitical scienceEngineering ethicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Each of the fields of conservation social science has made and can make a unique contribution to understanding the relationship between humans and nature and to improving conservation outcomes. Conservation scientists, practitioners and organizations recognize the importance of the conservation social sciences and are increasingly engaging in and funding conservation social science research. Yet conservation organizations and funders often lack a clear understanding of the breadth of the conservation social sciences, the types of questions that each field of conservation social science poses, the methods used by disciplinary specialists, or the potential contribution of each field of conservation social science to improving conservation practice and outcomes. Limited social science capacity and knowledge within conservation organizations may also mean that conservation practitioners and organizations looking to fund conservation social science research do not know where or how to begin defining a social science research agenda. This report presents a series of papers that were given as part of a workshop titled “The conservation social sciences: Clarifying ‘what?’, “how?’ and ‘why?’ to inform conservation practice” that occurred at the North American Congress for Conservation Biology in Missoula, Montana in July 2014. The workshop brought together specialists from the breadth of the conservation social sciences to define the contributions of their disciplines and fields to conservation through exploring the ‘what?’, ‘how?’ and ‘why?’ of each area of expertise. The resultant report aims to stimulate dialogue among conservation organizations, foundations, agencies, practitioners and researchers about the role of the conservation social sciences. It is intended to build capacity, promote knowledge and foster engagement with conservation social sciences in order to improve conservation practice and outcomes. The first chapter of the report introduces the conservation social sciences. The body of the report provides succinct synopses of the different conservation social sciences by specialists in Psychology, Economics, Sociology, Anthropology, Political Science and Governance, Human Dimensions, Political Ecology, Ethics, Education and Communication, Conservation and Development, and Science and Technology Studies. The concluding chapter a) provides a broad overview of the topics explored, questions asked, methods used and contributions made by each field of conservation social science and b) presents a process by which conservation organizations or funders can define and prioritize a conservation social science research agenda. We propose five steps to guide organizations wishing to better employ the conservation social sciences: 1) Recognize and overcome organizational barriers to incorporating conservation social sciences and build support for and understanding of the conservation social sciences; 2) Identify the conservation problem(s) that the organization aims to address and highlight their social dimensions; 3) Partner with social scientist(s) to frame key topics, questions and approach; 4) Brainstorm key topics for investigation or research questions and prioritize them to establish a conservation social science agenda; and 5) Partner with, contract or hire conservation social scientist(s) to carry out the work.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.002
Scholarly communication0.0010.001
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 head0.083
GPT teacher head0.296
Teacher spread0.213 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

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