Linking marine and terrestrial ecosystem services through governance social networks analysis in Central Patagonia (Argentina)
Bibliographic record
Abstract
The complex relationship among diverse natural factors in a given ecosystem and with society could be not explicitly reflected in governance actions and policy. Social networks are useful tools to characterize these links but few studies include social and ecological nodes. We applied social network analysis to characterize governance and use networks in a coastal socio-ecological system while testing (i) if governance links reflects ecosystem services (ES) use links, (ii) if social links reflect ecological relations between continental and marine ES and (iii) if relations among social actors are associated with their use of and participation in the management of ES. We use structured interviews to build one-mode use and governance networks with social actors and two-mode networks relating social actors and ES. Our results showed cohesive, low density and centralized networks of governance and use. We found that actor–actor links reflect ecological relations between continental and marine environment, but actor–actor relations are weakly correlated with those derived from actor–ES relations, meaning that actors with common interest about ES are no necessarily working together. This paper also shows that social networks are useful to highlight gaps and paths to move the system toward more effective co-management structures.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".