Effect of Fisheries Subsidies Negotiations on Fish Production and Interest Rate
Bibliographic record
Abstract
We analyze the effect of fisheries subsidy negotiations on financial markets and aggregate demand in developed and developing countries. We examine the plausible scenarios that are likely to emerge in the event of elimination or reduction of subsidies, and the subsequent effect on the financial markets and the fish production. We use the Keynesian macroeconomic static framework, which is based on an extended well-known investment-savings (IS) and liquidity preference–money supply (LM) model for analysis. Our analysis shows that the impact of a reduction in fisheries subsidies would reduce the exploitation of fish and marine resources in developing countries, thus leading to a general increase in fish prices and quantity stabilizing at lower levels. We also find that this effect would transfer to financial markets, leading to a decline in interest rates for fish exporting developing countries, but interest rates tend to stabilize at higher levels for fish importing developed countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".