Audience segmentation approach to conservation messaging for transforming the exotic pet trade
Bibliographic record
Abstract
Abstract Advancing transformative change for sustainability requires population‐wide behavior change. Yet, many behavioral interventions tackling environmental problems only examine average effects on the aggregate, overlooking the heterogeneous effects in a population. We developed and preregistered a novel audience segmentation approach to test the diverse impact of conservation messaging on reducing demand for exotic pets (private action ‐ i.e., desire to own exotic pets or visit wildlife entertainment places) and fostering citizen engagement for system‐wide change (civic action ‐ e.g., signing a petition or participating in a protest against the exotic pet trade). Through an online survey with US participants ( n = 2953), we identified 4 population segments (early adopters, early majority, late majority, and laggards), representing varying levels of commitment to wildlife conservation and then randomly assigned each segment to one of 3 messaging conditions. Messages highlighting negative consequences of the exotic pet trade and the power of collective action for system change effectively promoted private action among all segments except early adopters ( η p 2 = 0.005). Among civic actions, only the collective action message motivated early adopters and the early majority to sign petitions ( φ C = 0.193 and φ C = 0.097, respectively). Furthermore, the 4 segments showed distinct reasoning for action and inaction on wildlife conservation, with certain relational values, such as care, serving as both motivations and barriers to action. These findings highlight the need for targeted behavioral interventions across diverse populations.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".