Examining the effect of economic development, region, and time period on the fisheries footprints of nations (1961–2010)
Bibliographic record
Abstract
Anthropogenic activities are impacting marine systems, and the future sustainability of many global fisheries are in serious question. Our analysis draws on prior research in environmental sociology and food systems to better understand the association between economic development and the ecological footprint of fisheries. We provide a series of models to make comparisons across all nations, distinguishing between less-affluent nations and affluent nations over a 50-year period. We focus our analysis on the fisheries footprint of less-affluent nations to further explore how the effect of economic development varies across levels of national economic prosperity, region, and time period. The results of the study indicate that, over time, economic development is increasingly driving the fisheries footprint in less-affluent nations. Because this effect does not occur in affluent nations, we posit that less-affluent nations suffer the ecologically deleterious consequences of economic development more acutely. Furthermore, by utilizing post-estimation techniques for easier comparisons, our findings suggest that the magnitude of economic development’s effect on fisheries is strongest in more recent decades. Our findings also reveal that the effect of economic development is modified by region, as it has a stronger effect on fisheries footprint for less-affluent nations in Central and South America, but weaker in the Middle East and Africa. We conclude with a discussion of the implications for marine sustainability and the challenges posed by an environmentally intensive world capitalist food system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".