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Record W4394931265 · doi:10.1080/00219266.2024.2332728

Plants and the Kunming-Montreal global biodiversity framework: educational approaches to support pro-conservation behaviours

2024· article· en· W4394931265 on OpenAlexaboutno aff
Bethan C. Stagg, Justin Dillon

Bibliographic record

VenueJournal of Biological Education · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsBiodiversityBiodiversity conservationEnvironmental resource managementGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

The COP15 Kunming-Montreal Global Biodiversity Framework (GBF) aims to address rapid biodiversity loss and protect 30% of the planet for nature by 2030. Biology education could help to engage citizens with the GBF and promote pro-conservation behaviours, particularly for plants, which have historically been neglected in education and biodiversity conservation. In this narrative review and critical commentary, we consider what kind of approaches could lead to pro-conservation behaviour towards plants in support of the GBF, at school and tertiary levels of biology education. We examined twenty-two experimental studies about plant ecology and education, published in indexed journals from 1998 to 2022. The prevalent approaches were ecological gardening, informal science education and authentic enquiry. We assess the studies for the capabilities, opportunities and motivations provided by the pedagogic design and present behavioural models of how the approaches might support citizens’ pro-conservation behaviours, based on study findings. We discuss how the approaches might support the goals of the GBF, barriers to implementation and recommendations for overcoming these.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

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

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 head0.170
GPT teacher head0.355
Teacher spread0.185 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2024
Admission routes1
Has abstractyes

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