Individual and collective political efficacy predict farmer engagement and support for groundwater policies: implications from the California Sustainable Groundwater Management Act
Bibliographic record
Abstract
Common-pool resource theory suggests that the direct participation of local natural resource users in the management of common-pool resources can lead to effective management regimes. Nevertheless, the drivers of participation in common-pool resource management, including policy decision processes, and the effects of participation on stakeholder attitudes and policy preferences are relatively understudied. Here, we combine the social-ecological system (SES) framework with the political science concept of political efficacy to examine both contextual and personal drivers of farmer participation in California, USA’s 2014 Sustainable Groundwater Management Act (SGMA), as well as the effect of participation on support for policy mechanisms from the SGMA. We surveyed a total of 553 farmers in three counties across the California Central Valley and Central Coast. Overall, we find that < 50% of the farmers surveyed have participated in any SGMA-related events, with attending a meeting being the most common (45%), and testifying before a board being the least common (6%). Participation in any type of SGMA policy event was associated with multiple characteristics of the groundwater SES context, including the resource system (farm size) and actor attributes (farm bureau membership and receiving information about the policy), that likely combine to indicate a higher level of social, financial, and built capital. Higher participation was also associated with higher internal efficacy ratings, i.e., an individual’s self-assessment of their ability to understand and participate in the political process. Higher levels of internal efficacy were also correlated with support for both incentive- and regulatory-based policy mechanisms, as well as the perception that groundwater impacts are occurring now or soon, and exclusive reliance on groundwater. These results demonstrate that political competence and experience with policy processes and programs are not only associated with participation in current policy issues, which is widely recognized in existing research, but are also associated with policy mechanisms, in particular, with potentially more costly regulatory-based mechanisms.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".