Best for pleasure, not for business: evaluating recreational marine fisheries in West Africa using unconventional sources of data
Bibliographic record
Abstract
Abstract West African recreational fisheries, previously overlooked, are often assumed to be insignificant, yet they are increasingly present on social media given anglers’ tendencies to document their experiences. It is important to catch the trend early on as recreational fisheries develop in order to support their sustainable development and to make the most of the alternative economic opportunities that they offer. Here, the recreational fisheries of 11 West African countries are assessed using tourist records from YouTube, blogs and other unconventional records. We introduce the concept of “Recreational-to-Commercial Ratio (RCR)”, that is, the market-equivalent value per tonne of recreational fish injected to the economy, which is similar to “willingness to pay” for fish caught for recreation. Since the recreational fisheries of West African countries gained popularity in the last few years, catches increased and reached a total of 34,000 t annually, none of which was reported in official fisheries statistics. Recreational catches through a total annual revenue of US$152 million had an RCR of approximately 7, which means that developing recreational fisheries would increase the value of fish (whether caught or released) sevenfold. These findings could have major implications for the economy and conservation of fish stocks in West African countries.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".