Deliberative Q-method: A combined method for understanding the ecological value of urban ecosystem services and disservices
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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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 | 0.199 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".