Strategies for Improving Labor Dispute Resolution Mechanisms under the Application of Artificial Intelligence
Bibliographic record
Abstract
The construction of harmonious labor relations is of great significance in improving the quality of public services and promoting social harmony and stability.The study uses multi-period DID algorithm to construct a mathematical model of artificial intelligence application and labor dispute resolution, and conducts research on the influence relationship between the two.Aiming at the lack of preventive mechanisms for labor dispute resolution at present, principal component analysis and artificial neural network are used to establish a labor relations early warning model.The results show that artificial intelligence application has a significant positive impact on labor dispute resolution at the 5% level, and there is regional heterogeneity.The prediction accuracy of PCA-ANN model on labor relations in the training set and test set is 81.25% and 85.71%, respectively, which presents a good effect of early warning of labor relations, and it can be used to improve the mechanism of labor dispute resolution.Finally, based on artificial intelligence technology, the online labor dispute resolution mechanism is proposed to prevent the escalation of labor disputes and improve the effectiveness of labor dispute resolution by focusing on prevention, secondary control and subsequent resolution.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".