The Importance of Trip Context for Determining Primary Angler Motivations: Are More Specialized Anglers More Catch-Oriented than Previously Believed?
Bibliographic record
Abstract
Abstract Most conclusions from general assessments of angler motivations indicate that noncatch motives are more important to anglers than catch motives. Such research usually assesses the general motivation structure by anglers. To assess both general and more context-specific angler motivations, we surveyed the same anglers from northeastern Germany using two phases of a complementary survey design. First, a 1-year diary was used to collect trip-specific information; second, a personalized mail survey was used to elicit context-specific motivation information. Anglers selected their most important motives for their most frequent trip–target species combination (i.e., context) from a list of 10 salient fishing motives. Anglers frequently cited catch motives as the most important across a range of target species, large-bodied species such as northern pike Esox lucius being primarily associated with trophy fishing. Some species (such as small-bodied cyprinids) were targeted for noncatch reasons, while others (such as European perch [also known as Eurasian perch] Perca fluviatilis) attracted anglers seeking a multitude of psychological outcomes. Five distinct angler types were identified based on similarity of prime fishing motivation, namely, trophy-seeking anglers; nontrophy, challenge-seeking anglers; nature-oriented anglers; meal-sharing anglers; and social anglers. Members of these angler groups were similar in demographics and general angling behaviors but differed with respect to several indicators of angler specialization, indicating that committed anglers are more catch-oriented than previously assumed. Received November 12, 2010; accepted May 26, 2011
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".