Social psychological bases for Stakeholder acceptance Capacity
Bibliographic record
Abstract
Wildlife managers often encounter stakeholder groups with differing beliefs about ideal population levels of wildlife and appropriate management actions toward wildlife. For example, hunters, farmers, foresters, and suburban homeowners often express different acceptance capacities for white‐tailed deer. Similarly, stakeholder groups often differ over managing Canada geese, black‐tailed prairie dogs, beaver, and other species. Understanding and responding to these different preferences is essential to the successful management of publicly owned wildlife. Researchers have examined beliefs about wildlife populations from perspectives including cultural carrying capacity, overabundance, risk perception, wildlife acceptance capacity, and normative beliefs. Each approach has contributed to our understanding of how beliefs about ideal wildlife population levels are based on a complex interaction among internal, psychological variables (values, beliefs); behavioral variables (occupation, past experience with wildlife); and situational specifics (wildlife species, abundance, management actions). A normative approach, based on social psychology's hierarchical model of human thought, can help explain and predict the determinants and consequences of stakeholder acceptance capacity. Research using the normative approach demonstrates how stakeholder acceptance capacity for wildlife populations and management actions can be influenced by psychological, behavioral, and situational variables. Additional investigation of stakeholder acceptance capacity and its determinants will allow for more confident generalization about stakeholder responses to different wildlife population levels and management actions, and will help identify conditions that are likely to generate intense conflict among stakeholder groups.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".