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Record W2115450228 · doi:10.1007/s13412-015-0272-6

Ocean use in Hawaii as a predictor of marine conservation interests, beliefs, and willingness to participate: an exploratory study

2015· article· en· W2115450228 on OpenAlexaff
Carlie S. Wiener, Genevieve Manset, Judith D. Lemus

Bibliographic record

VenueJournal of Environmental Studies and Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsYork University
FundersNational Science Foundation
KeywordsOutreachExploratory researchDemographicsAffect (linguistics)Environmental resource managementMarine conservationPerceptionPsychologyGeographySociologyPolitical scienceEnvironmental scienceSocial science

Abstract

fetched live from OpenAlex

Conservation outreach requires an understanding of the socio-ecological dynamics within specific environments and how they affect meaning given to efforts. Nationwide studies of human perceptions are important in typifying how people use and view the marine environment; however, these findings often ignore specific regional differences. The purpose of this exploratory study was to investigate whether demographics and ocean use predict environmental concerns, interest in learning, and ocean conservation in Hawaii. Drawing on data from the Ocean Topics Public Attitudes Survey ( n = 422), regression analysis was used to create four models that predict participant attitudes on ocean conservation factors. Significant relationships were found between gender, Native Hawaiian ethnicity, types of ocean use, and willingness to participate in conservation activities. Key methodological approaches and findings are shared with the goal of informing better design and implementation of outreach to help understand ocean user needs in Hawaii.

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 score0.059
Threshold uncertainty score0.326

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 science0.0000.001
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 head0.089
GPT teacher head0.294
Teacher spread0.205 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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