MétaCan
Menu
Back to cohort
Record W2917280572 · doi:10.24043/isj.74

Motivating sustainable behavior: waste management and freshwater production on the Caribbean island of Saint Barthélemy

2019· article· en· W2917280572 on OpenAlexaffvenue
Lillian Howell, Russell Fielding

Bibliographic record

VenueIsland Studies Journal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsSustainabilitySocioeconomic statusIncentiveGovernment (linguistics)Stewardship (theology)GeographySmall islandSmall Island Developing StatesNatural resourceCaribbean islandEnvironmental planningSustainable developmentSustainable managementCaribbean regionEnvironmental stewardshipEnvironmental resource managementSAINTNatural resource economicsBusinessEnvironmental protectionPolitical scienceLatin AmericansClimate changeEcologySociologyEconomicsHistory

Abstract

fetched live from OpenAlex

Saint Barthlemy (St. Barth) is a Caribbean island located in the Leeward Islands of the Lesser Antilles. The island's small size, lack of natural resources, socioeconomic features, and geographic isolation make it an interesting case study for matters of sustainability, specifically freshwater production and waste management. Interviews were conducted with residents of the island to determine the factors that motivate or discourage sustainable behavior with regards to these environmental issues. A sense of civic duty, the simplicity of environmental regulations, rapid communication and implementation, socioeconomic stability and affluence, and economic incentives each served to promote environmental stewardship and sustainable decision-making, while government priorities and persistent habits tended to hinder such behavior. The results of this study could be applied to other island communities with similar characteristics to determine how we, as a global society, can move towards a more sustainable future.

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 score0.293
Threshold uncertainty score0.864

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)0.0010.000

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 head0.239
Teacher spread0.229 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueIsland Studies JournalSame topicEnvironmental Education and SustainabilityFrench-language works237,207