Fish commoditization and the historical origins of catching fish for profit
Bibliographic record
Abstract
Humanity’s relationship with fish dates back to prehistory, when ancestral hominins evolved the capacity to exploit aquatic resources. The impacts of early fishing on aquatic ecosystems were likely minimal, as primitive technology was used to harvest fish primarily for food. As fishing technology became more sophisticated and human populations dispersed and expanded, local economies transitioned from hunter-gatherer subsistence to barter and complex trade. This set up a positive feedback ratcheting fishing technology, mercantilization, and the commoditization of fish. A historical narrative based on archaeology and documentary evidence follows the principal changes in fisheries through evolutionary, ancient, classical and medieval eras to modern times. Some local depletions are recorded from early fishing, but from the 1950s, massive impacts of serial depletions by size, species, area and depth are driven by commoditized fishery products. North Sea herring fisheries are described in detail. Today’s severely depleted wild fish populations reflect social institutions built on global markets that value fish predominantly as a consumptive commodity, risking future ecological integrity and human food security. To sustain global fisheries, decommoditization strategies that sustain human and ecosystem relationships with fish beyond their commodity value are needed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".