Exploring cultural differences in wildlife value orientations using student samples in seven nations
Bibliographic record
Abstract
Abstract Understanding differences in the way people think about wildlife across countries is important as many conservation challenges transcend jurisdictions. We explored differences in wildlife value orientations in seven countries: Australia, Canada, Germany, Japan, Malaysia, the Netherlands and Serbia. Standard scales assessed domination (prioritizing human well-being) and mutualism (striving for egalitarian relationships with wildlife). We used student samples (total n = 2176) for cross-cultural comparisons. Reliabilities of the wildlife value orientations scales were adequate in all countries. Relationships between demographics and wildlife value orientations were different across countries. Men were generally more oriented towards domination and less towards mutualism than women, except in Serbia, where it was the other way around. Estimated at the level of the individual (using ANOVA), wildlife value orientations varied across countries, with nationality explaining a larger portion of the variation in mutualism (21%) than domination (6%). Estimated at the level of countries (using multilevel modelling), effect sizes were comparable. Thought about wildlife has previously only been examined within single countries. This paper makes a new contribution to the conservation literature suggesting that wildlife value orientations vary by country, and are associated with demographic factors. For conservation practices, understanding national differences in the way people think about wildlife is crucial to understanding sources of conflict among practitioners. Such knowledge is also important to gain public support for conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".