Examining ecosystem services and disservices through deliberative socio-cultural valuation
Bibliographic record
Abstract
The deliberative socio-cultural valuation of ecosystem services (ES) and disservices (EDS) is an understudied area of ES and EDS research. Participatory methods have been applied to ES and EDS valuation, but little is known on how these approaches could reveal and form shared values and impact decision-making. This paper presents the deliberative socio-cultural valuation of the Jose Rizal Plaza in Calamba City, The Philippines. The study aimed to assess how stakeholders value the ES and EDS of the park and examine how these values change in different situations. Online focus groups were carried out, and in each, the participants were asked to distribute importance and concern points to the various park ES and EDS, respectively. The valuation exercise was performed six times, changing the source and constituency of the valuation, and introducing discussions. Results confirm significant differences in the values assigned to several ES and EDS across the valuation exercises. Varying the sources and constituencies proved useful in revealing the participants' shared assigned values. The participants share a high appreciation for enjoyment and spending free time, sports and physical fitness, relaxation and mental recreation, social relationships, and local identity and cultural heritage. For EDS, they share a significant concern only for the risk of anti-social behaviour. This type of valuation could be further explored using other parks and cities to test if it will have consistent results. For the Jose Rizal Plaza, spaces for sports should be maintained and security should be improved.
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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 | 0.058 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".