Evaluation model of auxiliary employability of special population based on AHP-FUZZY algorithm
Bibliographic record
Abstract
In order to realize the quantitative management of the quality of higher education, this paper puts forward an evaluation model of auxiliary employability of special people under the concept of public employment service based on AHP-FUZZY algorithm.The phase space distribution structure model of special people's auxiliary employability under the concept of public employment service is constructed, the index parameter set of special people's auxiliary employability under the concept of public employment service is established, the fuzzy association rule distribution set is constructed by principal component analysis and fuzzy parameter estimation, and the association rule characteristic quantity of special people's auxiliary employability under the concept of public employment service is extracted.Advanced statistical analysis methods, such as principal component analysis, big data fusion analysis and fuzzy detection model, are adopted to classify the multi-dimensional attribute features of special people's auxiliary employability under the concept of public employment service, and the data is partitioned and scheduled in the fuzzy clustering center according to the differences of statistical feature parameters of employability analysis reports, and the feature decomposition model of special people's auxiliary employability under the concept of public employment service is constructed.The auxiliary employability of special people under the concept of public employment service is fused by blocks and the regional structural parameters are reorganized.The binary structural characteristics of auxiliary employability analysis of special people under the concept of public employment service are reconstructed in the subspace fusion database.According to the reconstruction results, fuzzy clustering is carried out under principal component analysis and fuzzy parameter estimation, and the optimal evaluation of auxiliary employability of special people under the concept of public employment service is realized.Based on SPSS statistical analysis software and Matlab simulation tool, the empirical simulation analysis of the evaluation shows that the characteristic clustering of the evaluation of the auxiliary employability of special population under the concept of public employment service is good, the reliability of the ✉Corresponding author.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".