Reducing Human Disturbance to Atlantic Flyway Shorebirds Using Social Science Methods
Bibliographic record
Abstract
Human disturbance is a significant threat to shorebirds in North America. Disturbance can result in direct mortality or have long-term impacts on the survival of shorebirds. Land managers employ a variety of management techniques to minimize anthropogenic impacts on shorebirds, but because the Atlantic Flyway is ecologically and recreationally diverse, management can vary among sites. This thesis used social science methods to understand the extent to which human disturbance is managed and how human disturbance is managed. Specifically, we surveyed land managers and biologists in the U.S. and Canada portions of the Atlantic Flyway to examine potential disturbances, types of activities that are restricted, when restrictions occur, the perceived effectiveness of management techniques, public compliance with restrictions, and resource needs of managers. With the findings from this research, agencies and organizations that manage shorebirds can assess where to invest time, effort, and resources to reduce disturbance. We also used a survey of dog walkers to ascertain the benefits and constraints to leashing dogs near shorebirds because dog walking is one of the top-rated potential disturbances to shorebirds. Additionally, we sought to understand the personal and social norms related to dog walking and evaluated if a community-based social marketing (CBSM) approach would be enhanced by the addition of norms. Using a CBSM approach, we provided insights on strategies to promote voluntarily leashing of dogs near shorebirds. Through this thesis, we aimed to bridge the needs of people and the needs of shorebirds, in an effort to produce effective conservation outcomes.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".