Duck hunters and difficulty complying with harvest regulations
Bibliographic record
Abstract
Abstract Due to the steady decline of duck hunter participation, several studies have investigated means to bolster the duck hunter population. Researchers and wildlife professionals have assumed that simpler regulations would attract new and unconfident hunters to participate in duck hunting. In light of this, we sought to identify what portion of the duck‐hunting population had difficulty understanding species‐specific bag limits or complying with species‐specific bag limits in the field. We also sought to describe hunters who had difficulty complying with specific bag limits and how their difficulties were associated with elements related to demography, attitude, and behavior. We found most hunters had no difficulty understanding (82%) or complying with (74%) species specific bag limits, but flyway (χ 2 = 35.06, P < 0.01), number of ducks harvested (χ 2 = 9.76, P < 0.01), number of years hunted (χ 2 = 9.20, P < 0.01), and gender (χ 2 = 4.14, P < 0.05), were important to predicting hunter difficulty with compliance. Hunters who can overcome their difficulties understanding and complying with species‐specific bag limits may be more likely to be integrated into the duck hunting culture, and more likely to continue duck hunting in the future. More species identification tools and fewer species‐specific bag limits may be appropriate for the 18% of the duck hunter population who indicated that bag‐specific regulations were difficult to understand and the 26% who indicated that it was difficult to comply with species‐specific bag limits in the field. A closer look may be warranted for how the trade‐offs associated with the combination of species‐specific bag limits in combination with the variety of duck season zone and split options states employ, license/stamp requirements, area‐specific regulations, and trespass laws may influence duck hunter experiences.
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 | 0.003 | 0.019 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".