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Record W1965199763 · doi:10.3727/154427207783948829

Public Awareness, Education, and Marine Mammals in Captivity

2007· article· en· W1965199763 on OpenAlexaboutno aff
Yixing Jiang, Michael Lück, E. C. M. Parsons

Bibliographic record

VenueTourism Review International · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsVisitor patternCaptivityPopularityWelfareTourismAnimal welfareEnvironmental educationRecreationPublic educationPsychologyPublic relationsEnvironmental resource managementEcologyPolitical scienceBiologySocial psychologyPublic administration

Abstract

fetched live from OpenAlex

Increasing popularity of marine parks as tourist attractions brought with it a number of concerns. Considerable attention has been paid to investigate issues, such as the ethics of keeping marine mammals in captivity, welfare of captive marine mammals, and the educational and conservational abilities of marine parks. Little research has been conducted to explore the public's awareness and opinions of these issues. Public awareness is an important tool to understand the quality of a marine park's products and services. This study was designed to investigate the public's awareness of welfare of captive marine mammals, educational and conservational purposes of marine parks, and to examine public awareness and opinions of Dunlap and Van Liere's New Environmental Paradigm. A total of 120 respondents from St. Catharines, Canada completed either a visitor or a nonvisitor questionnaire. Results indicated that most people were aware of the issues of welfare of captive marine mammals and educational opportunities offered by marine parks, but showed less awareness of the conservational issues. However, results also indicated that respondents were well aware of, and agree with, the concerns expressed in the New Environmental Paradigm.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.991

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)0.0100.000

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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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.

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

Citations36
Published2007
Admission routes1
Has abstractyes

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