Decision biases and environmental attitudes among conservation professionals
Bibliographic record
Abstract
Abstract The importance of human behavior in biodiversity conservation is widely recognized, but there is little published evidence about how conservation professionals make decisions when conservation values are at stake. We take a behavioral economics approach, administering simplified decision problems (“choice experiments”), questions about choice‐relevant preferences and views (“elicitation questions”), and a psychometric scale (the New Ecological Paradigm scale) to a difficult‐to‐recruit sample ( n = 100) of Canadian professionals involved in managing Rangifer tarandus caribou (Woodland Caribou). Our choice experiments reveal the importance of several decision biases (risk aversion, commission bias, and a bias towards fairness) in this influential group of conservation stakeholders. We then examine in‐sample differences between categories of professional affiliation (e.g., resource industry, environmental nongovernmental organization, or federal/provincial government), finding significant variation in responses to one elicitation question (reference points) and in psychometric scores. We discuss the implications of our findings for choice in conservation practice and for multistakeholder conservation policy. Comparing our findings to prior work on choice under uncertainty in nonconservation contexts suggests a possible replication problem in applying behavioral science insights to conservation problems, pointing to the need for a systematic research program. Results from development testing with a convenience sample of university students are presented for comparison throughout the study.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".