Ocean use in Hawaii as a predictor of marine conservation interests, beliefs, and willingness to participate: an exploratory study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".