MANAGING WATER QUALITY IN AHETEROGENEOUS LANDSCAPE : A SOCIAL NETWORK PERSPECTIVE
Bibliographic record
Abstract
Understanding how humans and ecosystems interact across landscapes is an importantchallenge for the development of sustainable societies. Human dominated landscapes arefrequently heterogeneous in their distribution of ecosystems and the associated goods andservices. It can be difficult to create management strategies that cater to diverse demandsfrom different resource managers, while at the same time promoting healthy functioningof ecosystems held in common. I use a social network perspective to analyze howmunicipal management units connect to each other with regards to a water resource intwo watersheds in Québec, Canada. I test the importance of collaborative network ties formunicipalities’ engagement in water quality management activities. I assess ifmunicipalities with different ecosystems, namely agriculture and tourism, engagedifferently in water quality management activities and if they have different socialnetworks. I assess the role of third party actor groups such as Government Ministries andNon-Governmental Organizations that connect municipalities across the diverselandscape. Third party actor groups are instrumental in connecting municipalities acrossa diverse landscape. Municipalities with ecosystems facilitating tourism have morecollaborative ties in the water quality management network and are more engaged inwater quality management activities than municipalities managing for agriculturalproduction. An asymmetry in collaborations and activity engagement for water qualitymanagement has implications for the capacity of the region to encourage basin scalewater management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".