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Record W2794794664 · doi:10.1080/13504622.2018.1450849

Metrics and outcomes of conservation education: a quarter century of lessons learned

2018· article· en· W2794794664 on OpenAlexaboutno aff
Rebecca Thomas, Tara L. Teel, Brett L. Bruyere, Samantha Laurence

Bibliographic record

VenueEnvironmental Education Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsVariety (cybernetics)General partnershipScale (ratio)Environmental educationProgram evaluationQuarter (Canadian coin)Environmental resource managementPsychologyBusinessPolitical scienceComputer scienceGeographyPedagogyEconomics

Abstract

fetched live from OpenAlex

We conducted a systematic literature review to analyze evaluations of conservation education programs on a global scale in order to better understand (1) temporal and spatial trends in conservation education program evaluations over the last 25 years, (2) patterns in the types of conservation-related issues addressed through these programs, (3) metrics that indicate effectiveness of conservation education programs, and (4) methods and timeframes used to draw conclusions about program outcomes. Findings indicated that there is a need to better connect the types of issues addressed through conservation education programs with metrics that would indicate success in addressing these issues and the actual outcomes measured and reported. As well, there is an opportunity to employ a variety of metrics and methods for evaluating program outcomes, particularly in developing countries, by focusing on cognitive and behavioral components as well as social and ecological ones. Finally, shifting to a more comprehensive strategy for evaluating multiple outcomes in different cultural contexts would provide opportunities for utilizing mixed methods and qualitative approaches in partnership with community stakeholders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.046
GPT teacher head0.401
Teacher spread0.355 · 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

Citations81
Published2018
Admission routes1
Has abstractyes

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