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Record W4293060564 · doi:10.1089/eco.2021.0067

Biospheric Values Predict Ecological Cooperation in a Commons Dilemma Scenario

2022· article· en· W4293060564 on OpenAlexaffabout
Adam C. Davis

Bibliographic record

VenueEcopsychology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsLakehead UniversityNipissing University
Fundersnot available
KeywordsCommonsDilemmaSocial dilemmaValue (mathematics)Common-pool resourceSocial value orientationsPsychologySocial psychologyEcologyEconomicsMicroeconomicsBiology

Abstract

fetched live from OpenAlex

In ecological commons dilemma research, environmental values tend to be treated as a monolith. However, environmental values vary and they do not equally predict proenvironmental behavior. In this study, we investigated the impact of three kinds of proenvironmental values (egoistic, altruistic, and biospheric) on competitive and cooperative behavior in a hypothetical ecological commons dilemma scenario. Two hundred Canadian undergraduate students completed an online survey assessing proenvironmental value orientation and commons dilemma decision-making tendencies. In line with our hypothesis, controlling for demographic characteristics (e.g., gender) and key facets of social desirability (e.g., impression management), egoistic, altruistic, and biospheric values positively predicted competition, altruistic cooperation, and ecological cooperation, respectively, within the commons dilemma. Results show that to promote the sustainable consumption of shared ecological resources, it is prudent for educators, environmental managers, and policy makers to encourage the expression of biospheric values.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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.

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

Citations6
Published2022
Admission routes2
Has abstractyes

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