Individual differences predict endorsement of water resilience
Bibliographic record
Abstract
In the epoch of the Anthropocene change, complexity, and uncertainty create a demand for new systems of water management and governance. One such management model that is rapidly gaining traction amongst both scholars and practitioners is the concept of water resilience. Although increasing attention has been paid to the overarching theoretical and applied issues surrounding water resilience, few have examined individual attitudes and perceptions towards this concept. In this paper, we examine to what extent individuals endorse - that is, agree with and see the importance of using - social-ecological resilience as a framework for management and governance of water resources. We approach the problem and promise of water governance in this way because individuals' mindsets (and shifts in mindsets) offers one of the most effective leverage points for larger system change. To explore water resilience endorsement, we developed a scale (i.e., a water resilience scale) that was designed to capture individual endorsement of each of the seven principles of social-ecological water resilience. Three additional sets of questionnaires were also used to examine whether individual characteristics (i.e., demographics, psychological factors, and environmental attitudes) predict water resilience endorsement. Overall, there was considerable societal endorsement of water resilience. However, the degree to which individuals endorsed the concept of water resilience differed as a function of demographics, psychological characteristics, and attitudes toward the environment. Future research should examine the nuances of endorsement and consider targeted approaches to influence endorsement levels by using the predictor variables as a basis for engaging and shifting mindsets.
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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".