What determines respondents’ valuation uncertainty? Impact of subjective perceptions from the demand and supply sides
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 Based on a contingent valuation method survey on air quality improvement in northern China, we construct several subjective perception determinants of respondents' valuation uncertainty from both the demand and perceived supply sides. Using the individual-level uncertainty measurements initially proposed by Wang and He (2011) and their alternative transformations, we analyze how these factors of demand and perceived supply sides affect people's valuation uncertainty. Our results demonstrate the significant contribution of these determinants in explaining respondents' uncertainty. On the demand side, people who ‘don't know much’ about benefits-related factors have the highest level of uncertainty, and those claiming to ‘know nothing’ most often report the lowest level of uncertainty. On the supply side, people who either do not trust or are not satisfied with the control policies tend to be more certain of their valuation. The subsequent analyses also suggest that these results be interpreted as negative certainty, which is attributed to a lack of interest.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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