Decision biases and environmental attitudes among conservation professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| 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 | 0.003 |
| 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 it