Analysis of Structural Model of Chinese College Counselors' Core Literacy
Bibliographic record
Abstract
College counselors in China are important key members for ideological and political work. The quality of college counselors will directly affect the development of college students in all aspects. Most predecessors only conduct theoretical analysis on counselors’ literacy, which lacks the support of empirical research. Based on literature analysis and interviews, this paper analyzes the content of counselors' core literacy, screens predictive questionnaire projects, and compiles formal questionnaires. With an investigation of 537 college teachers and students, this paper builds a model of core literacy for counselors, which helps people directly understand the characteristics of university management in China and understand the standards of Chinese universities for their managers. The results show that: (1) The test has strong reliability and validity. Cronbach’s α coefficient and the tests’ split-half reliability coefficients are all above 0.95. The coefficients of internal consistency and the split-half reliability of each dimension are all above 0.85. The Χ2/df of analytical results of verification factors is less than 5. GFI, AGFI, NFI, CFI, and IFI are all greater than 0.7. RMSEA is equal to 0.07. The fitting indicators of this model are sound and this test meets measurement requirements. (2) The core literacy structure of college counselors consists of five major factors: moral quality, professional competence, political awareness, instructional planning, and interpersonal relationships. The total variation rate of these five factors is 65.117 percent. (3) Among the five factors, moral quality is the most influential factor.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".