Small is beautiful, but large is certified: A comparison between fisheries the Marine Stewardship Council (MSC) features in its promotional materials and MSC-certified fisheries
Bibliographic record
Abstract
The Marine Stewardship Council (MSC) sets a standard by which sustainable fisheries can be assessed and eco-certified. It is one of the oldest and most well-known fisheries certifications, and an estimated 15% of global fish catch is MSC-certified. While the MSC is increasingly recognized by decision-makers as an indicator for fishery success, it is also criticized for weak standards and overly-lenient third-party certifiers. This gap between the standard's reputation and its actual implementation could be a result of how the MSC markets and promotes its brand. Here we classify MSC-certified fisheries by gear type (i.e. active vs. passive) as well as by length of the vessels involved (i.e. large scale vs. small scale; with the division between the two occurring at 12 m in overall length). We compared the MSC-certified fisheries (until 31 December 2017) to 399 photographs the MSC used in promotional materials since 2009. Results show that fisheries involving small-scale vessels and passive gears were disproportionately represented in promotional materials: 64% of promotional photographs were of passive gears, although only 40% of MSC-certified fisheries and 17% of the overall catch were caught by passive gears from 2009-2017. Similarly, 49% of the photographs featured small-scale vessels, although just 20% of MSC-certified fisheries and 7% of the overall MSC-certified catch used small-scale vessels from 2009 to 2017. The MSC disproportionately features photographs of small-scale fisheries although the catch it certifies is overwhelmingly from industrial fisheries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".