A discriminant analysis of social and psychological factors influencing fishing participation
Bibliographic record
Abstract
Lake Ontario, one of North America?s Great Lakes, provides coastal residents of New York State with a sportfishery integral to both local traditions and the economy. Recent and projected declines in the number of state residents fishing Lake Ontario have generated concerns among fishery managers and business owners. In order to identify management and marketing strategies that can be used to increase fishing participation, an understanding of the influence of social and psychological factors on participation during childhood, adolescence, and adulthood is needed. Examination of both existing angler market groups (e.g., males) and market groups with growth potential (e.g., females) could provide further insight into increasing participation. This study identifies the social and psychological factors that influenced fishing participation for a sample of 1,050 Lake Ontario anglers (i.e., 525 males and 525 females). A mail survey, based on the elements included in a wildlife recreation involvement model by Decker et al. (1987), was conducted in 2001. Discriminant analysis was used to quantify the influence of these elements on fishing participation for males and females during childhood, adolescence, and adulthood. Elements identified as strongly influencing fishing participation for both males and females were opportunity, perceived ability, and fishing-related customs during childhood; affiliation, opportunity, and commitment during adolescence; and affiliation and commitment during adulthood. Based on study results, management and marketing strategies for increasing fishing participation were developed.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".