Take Measures: Knowledge Poverty Alleviation for Contiguous Poor Areas―A Case Study of the Wuling Mountain Area
Bibliographic record
Abstract
Knowledge poverty alleviation aims to help poor areas and poor people out of poverty by strengthening the knowledge infrastructure, enhancing the professional skills and “self blood” feature of the poor, establishing cultural knowledge networking and activating poor self-consciousness of poverty-stricken areas. Based on combing the theory of poverty at home and abroad and outlining research status and the analysis of the Wuling Mountain Area, the paper presents main primary paths of Knowledge for the contiguous poor areas, aiming at providing theoretical support and basis for decision-making for all levels of the government to lay our new contiguous destitute poverty alleviation during the battle and build a comprehensive well-off society and realize the country’s political stability, national unity, border consolidation, social harmony, ecological security. Simultaneously, a further objective of this paper is to attract the attention of more scholars at home and abroad to knowledge poverty alleviation which is important proposition and build up a systematic theoretical framework from the theoretical and practical dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".