Relational Values of Cultural Ecosystem Services in an Urban Conservation Area: The Case of Table Mountain National Park, South Africa
Bibliographic record
Abstract
This paper assesses how residents of a developing city in the Global South, recognize and value the multiple diverse cultural ecosystem services associated with freshwater ecosystems, as provided by different landscape features originating in an urban protected area. This objective was achieved by establishing who benefits from freshwater ecosystem services, uncovering the spatial and temporal relationships these beneficiaries have with landscape features, and determining the relational nature of ecosystem service values, benefits and trade-offs as experienced by the different users. Recreation, aesthetic and existence services were valued highest by respondents. People who live closer to the park use, and benefit from, the park’s freshwater ecosystems more frequently than those living further away. Park visitors want ease of access in terms of distance to specific freshwater ecosystems, and then once there, they want a diversity of activity options, such as recreation opportunities, as well as places to reflect and meditate. This study of cultural ecosystem services improves our understanding of social-ecological systems in urban areas by exploring the relationships between park and people which can guide management to ensure equitable and sustainable ecosystem service provision to all city residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".