Deliberative Q-method: A combined method for understanding the ecological value of urban ecosystem services and disservices
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
There is a need, in the ecosystem valuation literature to compliment the economic methods with sociocultural valuation methods that capture and facilitate a better understanding nuanced social and cultural values that are difficult to measure. Yet, sociocultural valuation methods are often critiqued for their lack of structured and replicable procedures and for often maintaining limited internal research validity. Accordingly, this paper demonstrates the development and application of a mixed-methods valuation approach to better recognize non-use social and cultural values by integrating the triad of deliberation, local ecological knowledge, and value quantification. We operationalized this method in Amman, Jordan where we analyzed how local experts value, based on their local ecological knowledge, the ecosystem services supplied by the City's urban water features (fountains, ponds, and streams).•We combine the conventional Q-method and focus group to yield a group deliberative Q-method.•The deliberative Q-method facilitates a structured valuation framework.•The deliberative Q-method method produces rich qualitative data.•The rigorous statistical analysis of deliberative Q-method improves internal validity and streamlines qualitative data coding.•The rigorous statistical analysis of deliberative Q-method weighs competing values to better understand polarized and consensus views.
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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 | 0.028 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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