Modernization, Political Economy, and Limits to Blue Growth: A Cross‐National, Panel Regression Study (1975–2016)*
Bibliographic record
Abstract
Abstract Seafood production and trade have expanded dramatically over the last 40 years and comprise one of the fastest growing, and most environmentally impactful, sub‐sectors of the global food system. While richer nations have increased their seafood consumption and displaced their environmental load, the marine environmental impact of fishery production has largely shifted to the waters of less‐affluent nations. To sustain fishing economies and seafood security, in an era of increasing marine ecological precarity constitutes a major challenge for development and human well‐being in the 21st century. Blue growth perspectives emphasize the transformative power of growth‐oriented development. Such perspectives conflict with critical political economic theories of environment and food systems; notably, the treadmill of production and world food system scholarship. Using annual data from the Global Footprint Network, World Bank, UN FAO, and International Monetary Fund, this study applies methods in cross‐national, panel regression analysis in order to ultimately pose some important challenges to modernist blue growth perspectives. The analysis suggests that economic growth and incorporation into the world market economy have led to unsustainable and inequitable outcomes regarding the marine ecological impact of fisheries.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".