Mitigating collision-caused bird mortality through message framing: Insights from residents' intentions for bird-safe windows
Bibliographic record
Abstract
Efforts to change human behavior for the benefit of biodiversity often rely on the dissemination of scientific information about biodiversity loss to nonscientific publics. This approach to science communication, known as the knowledge deficit model, is often insufficient for changing behavior. Recent trends reveal a rise in the use of message framing as an alternative method of communication. To address biodiversity loss caused by bird-window collisions, we use experimental survey design to compare the effect of deficit model messaging and four message frames (efficacy, emotional, moral, normative) on residents' intentions to adopt bird-safe windows, while accounting for other potentially influential cognitive and contextual factors. Data from a sample of bird enthusiasts ( n = 2854) and the general public ( n = 2054) in the United States and Canada indicate efficacy and emotional message frames were the most effective for bird enthusiasts and the general public, respectively. Prior experience with collisions, perceived impact of collision prevention, centrality of birding, educational attainment, and mutualist wildlife value orientations were also positively associated with respondents' intention to adopt bird-safe windows. Normative message frames, age, identification as male, residency in the U.S., and domination wildlife value orientations were negatively associated with adoption intention. Beliefs about collision prevention, centrality of birding, age, education, and wildlife value orientations also had similar associations with respondents' intention to encourage others to make their windows bird-safe. Results provide important insights for the mitigation of collision-caused bird mortality through evidence-based message framing, and the actions people are willing to take on behalf of birds and biodiversity.
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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 | 0.000 | 0.001 |
| 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".