Factors influencing social demands of aquatic ecosystems
Bibliographic record
Abstract
Aquatic ecosystems provide services essential to human health and economies. Therefore, resource management programs aim to ensure the sustainable flow of these services. Stakeholder engagement is often a critical tool in learning what services are of priority to the public and may be integral to the success of aquatic ecosystem management because public participation in planning and decision making can generate broader support, e.g., financial, intellectual, and labor, for the management plan. The collection of such information may even be statutorily mandated, such as in the Clean Water Act of the United States, which requires that water bodies be classified for the beneficial uses, e.g., fisheries, drinking water, or recreation, they provide. Past evaluations of stakeholder engagement with aquatic ecosystems have considered a wide range of factors influencing engagement. We conducted a critical review of the literature on characteristics of stakeholders and characteristics of the environment that influence stakeholder engagement and participation with aquatic ecosystems. Our objective was to identify factors that should be considered in the creation of surveys to help encourage the inclusion of ecological and social beneficial uses data in large-scale water monitoring programs. Factors identified in our review were, extent and influence of place-based knowledge; proximity to, and frequency of visitation of the resource(s) being considered; basic demographics such as age, gender, education, and income; home community type; aesthetic appeal of the resource; and primary reason for engagement with the resource. We propose these factors, with subfactors, as a template for survey development.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".