The Relationship between Marine Biodiversity Conservation and Poverty Alleviation in the Strategies of Rural Development in China
Bibliographic record
Abstract
Shen, J.; Han, X.; Hou, Y.; Wu, J., and Wen, Y., 2015. The relationship between marine biodiversity conservation and poverty alleviation in the strategies of rural development in China.Biodiversity conservation and sustainable management of ecosystems should be included in eradicating poverty and achieving the internationally agreed goals related strategies. This study demonstrated the primary content of the Outline of Development-oriented Poverty Reduction for China's Rural Areas (DPRCRA). By employing the Participatory Rural Assessment approach, this study assesses the effectiveness of DPRCRA in improving the ecology, socio-economic conditions and biodiversity conservation, elaborating on the opportunities and challenges entailed in the outline strategy. Results indicated that the relationship between them was Poverty escalation cycle and Poverty alleviation cycle. The paper also discusses the role of developing appropriate institutional mechanisms to integrate conservation and development efforts from practitioners' perspective, to enable poverty alleviation and marine biodiversity conservation to succeed, and proposes a set of guiding suggestions for making policies on rural capacity building and enhancing compensation mechanism.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".