Including Management Policy Options in Discrete Choice Experiments: A Case Study of the Great Barrier Reef
Bibliographic record
Abstract
Information about the management policy used to achieve environmental protection outcomes is rarely included as variables in choice experiments. In cases where people have very different preferences for the types of input measures used, the utility of environmental protection options may be sensitive to the choice of inputs used to achieve the protection. The discrete choice experiment reported in this paper to value protection measures for the Great Barrier Reef in Australia is interesting in two important ways. First, different management policies to increase protection have been included as labels in the choice experiment to test if the mechanisms to achieve improvements are important to respondents. Second, the level of certainty associated with predicted reef health has been included as an attribute in the choice alternatives, helping to distinguish between outcomes of different management policies. The results show that protection values vary with the policy scope of the improvements being considered. Values are sensitive to whether protection will be generated by improving water quality entering the reef, increasing conservation zones or reducing greenhouse gas emissions, and the level of certainty of outcomes.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".