Architecture and Computational Optimization of Community Employment Support System for Digitally Intelligent Students Led by Digital Party Building Based on Field Theory
Bibliographic record
Abstract
In this paper, K-prototype algorithm is chosen to cluster and analyze the data of students' behavior in the educational field.Further, a model of students' employment interest is constructed based on the job rating data of different classes of students.The timeliness is introduced in the model to improve the recommendation accuracy.Synthesize the algorithm and model to build an employment support system.Apply the system to the clustering study of college students' behavioral data to verify its career recommendation value.Set up comparison experiments to find the optimal similarity fitting parameters and number of neighbors to improve the system recommendation accuracy and judge the system recommendation effect.Preliminarily divide students into 3 categories by analyzing students' online behavior and book borrowing behavior.Preliminarily categorize students into 4 categories based on their grades.Combined with the performance labels and grade categories of professional courses, the employment direction of students was finally clustered into four categories, namely "postgraduate entrance examination", "civil servant application", "company work" and "others".The highest accuracy of the system job recommendation is achieved when the similarity fitting parameter λ = 0.5 and the number of neighbors N = 50.The RMSE value of the K-prototype algorithm ranges from 0.6011 to 0.731, and the recommendation effect is better than the comparison algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".