Role of Fishery and Forest Resources in Local Economic Performance: Evidence from the Lake Zone of Tanzania
Bibliographic record
Abstract
The country’s economic development usually occurs at a local level where interactions between economic elements are highly dense. Even though economic development processes are highly localized; economic growth in the country is due to concentration in growth in a limited number of locations with unique comparative advantages. Lake zone of Tanzania is one of the potential economic areas in the country with unique natural resources (fisheries and forests) features. This study uses cross-sectional data in the lake zone of Tanzania to determine the impact of the fishery resources (which are highly abundant) and forest resources (which are least abundant) on the local councils’ economic performance, admitting other control variables. Multiple linear Regression analysis results indicate that fishery resources negatively affect the councils’ economic performance, with insignificant economic value. Forest resources in the area have a significant positive impact on the economic performance. This paper concludes that despite the abundant fishery resources in the area, they have an insignificant contribution to the local councils’ economic performance. Moreover, the least forest resources have a very low economic impact on local economic performance. Which therefore indicate the needy of reforming available strategies, policies and legal framework accordingly.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".