Disentangling the complexity of socio-cultural values of temporary rivers
Bibliographic record
Abstract
In the last decade, an awareness towards temporary rivers has increased globally in response to drying climates and growing human demand for water. However, social perceptions of temporary rivers have rarely been incorporated in their science and management. In this study, we advance an understanding of the socio-cultural values of temporary rivers principally in a European context. We used an ecosystem services-based approach for a participatory and deliberative exercise with 16 researchers and managers. Our results point out to two important aspects of socio-cultural values in temporary rivers. First, cultural ecosystem services have high socio-cultural values and usually represent the interests of the less influential stakeholders in related conflicts. And second, the temporal and geographical variability of these types of rivers is key to understand their socio-cultural values. As an example, the low provision of freshwater in a long non-flowing phase is one of the reasons for its high value. The results above point to future research needs that deserve more attention like the study of tradeoffs and synergies of ecosystem services and interdisciplinary research and management. We finally acknowledge the need to conduct case study research to account for geographical variation and to include the multiple views of different stakeholder groups.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".