Personal time quality as a transformative metric for assessing cultural ecosystem services
Bibliographic record
Abstract
Cultural ecosystem services (CES) are a policy priority under the Kunming–Montreal Global Biodiversity Framework; however, current approaches either commodify CES through monetary valuation or rely on plural, non-comparable metrics, leaving use and experiential quality poorly captured. We propose the subjective quality of personal time—how “well spent” or “wasted” moments feel in a place—as a universal, scalar indicator of CES use. We outline practical routes to measurement via participatory mapping (PPGIS), lightweight experience sampling, and passive digital traces, and show how geosocial collaborative filtering can translate ratings into place-based recommendations. Reframing CES as the increment to time quality provided by the environment resolves the tension between monetary valuation and non-monetary pluralism, yielding a single, comparable metric that remains grounded in lived experience. A proposed PPGIS 2.0 workflow can collect time-quality ratings, tag activities and landscape settings, and return immediate, personalised recommendations, creating continuous data streams rather than one-off surveys. Integration with remote sensing and existing mobility datasets enables mapping of CES potential where participatory data are sparse. A time-quality metric makes CES visible, comparable and actionable across contexts, aligning monitoring with the Global Biodiversity Framework and supporting transformative, people-centred decisions. It offers a generic, accessible message for non-specialists: manage places based on the quality of the time they enable, not merely the quantity of visits, and use participatory, data-driven tools to recommend, protect, and enhance those experiences.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".