Research on the Construction of Rural Primary Party Organizations in Jilin Province under the Background of Targeted Poverty Alleviation
Bibliographic record
Abstract
Starting from the connotation of precision poverty alleviation and the poverty alleviation tasks of Jilin Province, clarify the national poverty alleviation concept and poverty alleviation arrangements and the poverty alleviation situation of Jilin Province, as the realistic basis for the research; and according to the law of universal connection of things, explain the precision poverty alleviation and rural grassroots The dialectical relationship of the party organization unites the two as the theoretical basis for the research; on the basis of reality and theory, selected rural party building experience in some counties and townships in the province as individual cases, and integrated it into the rural grassroots party organization building in Jilin Province In the analysis, sort out specific measures to promote targeted poverty alleviation and strengthen the construction of rural grassroots party organizations, summarize the achievements, analyze the existing problems in the construction of rural grassroots party organizations in targeted poverty alleviation, and summarize and summarize the effects that affect the role of grassroots party organizations in targeted poverty alleviation. The cause of the problem, and then put forward specific countermeasures and suggestions from a general perspective.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".