Bibliographic record
Abstract
The three decades following World War II were a period of rapidly increasing fishing effort and landings, but also of spectacular collapses, particularly in small pelagic fish stocks. This is also the period in which a toxic triad of catch underreporting, ignoring scientific advice and blaming the environment emerged as standard response to ongoing fisheries collapses, which became increasingly more frequent, finally engulfing major North Atlantic fisheries. The response to the depletion of traditional fishing grounds was an expansion of North Atlantic (and generally of northern hemisphere) fisheries in three dimensions: southward, into deeper waters and into new taxa, i.e. catching and marketing species of fish and invertebrates previously spurned, and usually lower in the food web. This expansion provided many opportunities for mischief, as illustrated by the European Union’s negotiated ‘agreements’ for access to the fish resources of Northwest Africa, China’s agreement-fee exploitation of the same, and Japan blaming the resulting resource declines on the whales. Also, this expansion provided new opportunities for mislabelling seafood unfamiliar to North Americans and Europeans, and misleading consumers, thus reducing the impact of seafood guides and similar effort toward sustainability. With fisheries catches declining, aquaculture—despite all public relation efforts—not being able to pick up the slack, and rapidly increasing fuel prices, structural changes are to be expected in both the fishing industry and the scientific disciplines that study it and influence its governance. Notably, fisheries biology, now predominantly concerned with the welfare of the fishing industry, will have to be converted into fisheries conservation science, whose goal will be to resolve the toxic triad alluded to above, and thus maintain the marine biodiversity and ecosystems that provide existential services to fisheries. Similarly, fisheries economists will have to get past their obsession with privatising fisheries resources, as their stated goal of providing the proper incentives to fishers can be achieved without giving away what are, after all, public resources. Overall, the crisis that fisheries are now going through can be seen as an opportunity to renew both their structure—away from fuel-intensive large-scale fisheries—and their governance, and to renew the disciplines which study fisheries, creating a fisheries conservation science in the process. Its greatest achievement will be the creation of a global network of Marine Protected Areas, which, as anticipated by Ramon Margalef, is the way to make controlled exploitation compatible with the continued existence of functioning marine ecosystems.
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.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".