Recreational angler satisfaction: What drives it?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Satisfaction is the reward that recreational anglers receive from their experiences, and it constitutes a relevant management target. Angler satisfaction also shapes preferences for regulations, compliance with rules and general angler behaviours. Because of its central role in recreational fisheries management, it is important to understand what drives angler satisfaction. Our objective was to study the catch and non‐catch‐related determinants of recreational angler satisfaction using a standardized literature search and synthesizing the literature using meta‐analytical techniques. After identifying and screening 279 papers, we obtained K = 172 effect sizes extracted from N = 23 studies that met our inclusion criteria. A three‐level random‐effects model on Pearson's R, derived from studies relating component satisfaction to overall satisfaction assuming a sum‐of‐satisfaction model, was fitted. The aggregated effect sizes revealed that catch‐related (i.e. catch rate, size of caught fish, fish harvest) and two non‐catch‐related components (i.e. access to fishing sites and crowding) were most related to angler satisfaction. Other non‐catch components (e.g. environmental quality, facilities, perception of relaxation quality) also contributed to angler satisfaction but were of less importance, more variable across studies and in some cases not significant (e.g. perceived water quality, quality of social experience). We conclude changes to access to fishing sites, crowding and a reduction in catch qualities, will in many cases produce dissatisfied anglers. In the absence of local studies, focusing management attention on these components can be recommended if the aim is to satisfy anglers or avoid managerial or social issues that emerge from dissatisfied anglers.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it