Testing household preferences for the importance of the frequency and severity of water quality impairment
Bibliographic record
Abstract
Water quality indices are employed by governments largely as a means of communicating the multifaceted nature of water quality and aquatic ecosystem health to the general public. Given the complexity of responsibility for oversight of freshwater quality in Canada, the Canadian Council of Ministers of the Environment (CCME) has developed an index based on the severity, frequency, and scope of water impairment. An important feature, and potential shortcoming, of this approach is that the three attributes of water quality are weighted equally. If households, however, weight these attributes differently, then the index’s ability to convey information to the public may be weakened. This issue is examined by eliciting household preferences for a hypothetical water quality protection program that reduces the severity and frequency of impairment using a discrete choice experiment (issues of scope are not included in the analysis). Latent class and mixed logit models are estimated. The latent class models, which outperform the mixed logit, indicate the presence of two preference classes that hold dramatically different preferences for benefits of the protection program. While one group of respondents is unresponsive to the severity and frequency of impairment, there is evidence that the other group may assign different weights to the attributes. These findings suggest the CCME index could convey different information to the two groups.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".