Reconstructing overfishing: Moving beyond Malthus for effective and equitable solutions
Bibliographic record
Abstract
Abstract Inaccurate or incomplete diagnosis of the root causes of overfishing can lead to misguided and ineffective fisheries policies and programmes. The “Malthusian overfishing narrative” suggests that overfishing is driven by too many fishers chasing too few fish and that fishing effort grows proportionately to human population growth, requiring policy interventions that reduce fisher access, the number of fishers, or the human population. By neglecting other drivers of overfishing that may be more directly related to fishing pressure and provide more tangible policy levers for achieving fisheries sustainability, Malthusian overfishing relegates blame to regions of the world with high population growth rates, while consumers, corporations and political systems responsible for these other mediating drivers remain unexamined. While social–ecological systems literature has provided alternatives to the Malthusian paradigm, its focus on institutions and organized social units often fails to address fundamental issues of power and politics that have inhibited the design and implementation of effective fisheries policy. Here, we apply a political ecology lens to unpack Malthusian overfishing and, relying upon insights derived from the social sciences, reconstruct the narrative incorporating four exemplar mediating drivers: technology and innovation, resource demand and distribution, marginalization and equity, and governance and management. We argue that a more nuanced understanding of such factors will lead to effective and equitable fisheries policies and programmes, by identifying a suite of policy levers designed to address the root causes of overfishing in diverse contexts.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".