Industrial fishing and its impacts on food security: a systematic review
Bibliographic record
Abstract
This systematic review seeks to answer the question: how have previous studies conceptualized and measured food security in relation to industrial fishing? Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, initial searches yielded 983 publications, which were distilled to 55 relevant articles for in-depth analysis after the screening process. These studies span from 1997 to 2024, covering a diverse range of geographical contexts, and cover a variety of scales from local community impacts to national and global trends. Overall, four principal themes related to the perceived positive and negative and direct and indirect impacts of industrial fishing on food security were identified: (1) Industrial fishing activities provide jobs to local populations of which earnings are used to purchase other food items; (2) Industrial fishing activities provide fisheries products to local markets which are used as a common food source; (3) Industrial fishing activities damage the environment, leading to a decrease in the availability of catch for food or livelihood; (4) Industrial fishing activities outcompete local users and export catch to distant markets, thereby decreasing available food to local communities. The methodologies used in these studies mainly took a singular methods approach rather than a mixed-methods approach. Specific methodologies were rooted in diverse fields such as econometrics, policy, geography, fisheries science, and public health. The most frequently used data types were fisheries production, consumption, trade, economic, and fisher behavior data. A notable gap in the research is the lack of integration of complex data on industrial fishing, such as detailed catch records and fishing efforts, with the multifaceted aspects of food security, including detailed household consumption trends. This separation has often led to studies focusing on either fishing activities or food security outcomes in isolation, which can oversimplify the relationship between fisheries production and food security. The findings highlight the need for a more integrated research approach that combines fisheries or ecosystem data with a thorough examination of household consumption behaviors and broader food systems. Such an approach is essential for creating effective policies and interventions to support and improve the livelihoods of communities reliant on 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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".