An Exploratory Factor Analysis of Lake Ontario Resident Bass Angler Motivations, Constraints, and Facilitators
Bibliographic record
Abstract
Abstract\nIn 2009, SUNY ESF completed a survey of 7,000 property owners in the seven New York counties bordering Lake Ontario in order to examine the motivations, constraints, and facilitators related to resident bass fishing participation. The questionnaire included five-point scale questions on motivations and constraints/facilitators to fishing. Resident anglers returned 681 completed surveys. A short follow-up survey was distributed to all non-respondents and 264 additional responses were received from anglers. After data entry, bass anglers were identified based on respondents' preferences for either largemouth or smallmouth bass fishing. Two exploratory factor analyses were conducted: one for variables related to motivations and one for constraints/facilitators. The factor analyses yielded 10 motivation-related factors and 11 constraints/facilitators. Motivations for fishing were affiliation and nature appreciation/enjoyment. Factors identified by respondents as constraining fishing participation were lack of time and bad weather; factors found to facilitate fishing participation were good weather, current/past experience, and access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".