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Record W2902654949 · doi:10.1080/14660466.2018.1541679

Children as drivers of change: The operational support of young generations to conservation practices

2018· article· en· W2902654949 on OpenAlexaff
Corrado Battisti, Béatrice Frank, Giuliano Fanelli

Bibliographic record

VenueEnvironmental Practice · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCapital Regional District
Fundersnot available
KeywordsAgency (philosophy)BusinessEnvironmental resource managementStrengths and weaknessesEnvironmental planningGeographyPsychologySociologyEnvironmental scienceSocial psychology

Abstract

fetched live from OpenAlex

Children are the target of educational actions, both as passive subjects to whom conservation strategies are directed, and as active participants able to support operational management actions. Conservation actions driven by children can play an important role in critical social contexts where external (anthropogenic threats) and internal (organizational weaknesses) conditioning factors hinder effective protected areas management. In this article, we propose a case study where children involved in conservation practices offer operational support to overcome internal weaknesses of a park agency, motivate parks, staff and mitigate a series of anthropogenic threats. We used the “threat analysis” approach, derived by the Theory of Change, to define a causal chain linking ecosystem targets, human-induced threats, and conservation measures in a framework. We believe that conservation driven by children can be an innovative way to implement protected areas management in socially-degraded landscapes to the point of fostering local support for conservation.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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