Legal Regulation Strategies for Civil Abuse of Litigation Rights Based on Algorithmic Research
Bibliographic record
Abstract
The research selects the documents related to the legal regulation of civil abuse of rights of action as the research object, crawls the central and local legal regulation database through Python, and uses the social network analysis method to quantitatively analyze the dimensions of the subject of legal regulation from the composition of the subject of legal regulation, the density of the network, the centrality, and the cohesive subgroups, etc.The data preprocessing is carried out on the valid data obtained.Secondly, we pre-processed the acquired valid data, extracted high-frequency words using the improved TF-IDF algorithm, and obtained the probability distribution of the subject strength of "document-subject" and "subject-phrase-item" by calculating the degree of perplexity and utilizing the LDA subject model, and obtained the probability distribution of the subject strength at different stages of civil abuse litigation.In order to obtain the themes and evolution characteristics of the legal regulation of civil abuse of rights of action at different stages, the research results are combined with the results of the study from multiple dimensions.Finally, the research results are combined to design the strategy of legal regulation of civil abuse of rights of action from multiple dimensions.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".