Assessment of student’s talent management in a corporate university
Bibliographic record
Abstract
The purpose of this study was to evaluate the student's talent management of a corporate university in Iran by descriptive-analytic method.The statistical population of the study included all 2200 students of the university.Based on the estimated number at Morgan table, 202 respondents completed the survey instrument.The data collecting tool of the questionnaire was ascertained and its reliability was obtained 78 percent by Cronbach's alpha coefficient.Content validity of the tool was also verified by the experts.For data analysis, the binomial test and Structural Equation Modeling (SEM) were used.The results show that none of the components of talent management (deployment and employment, career progression path, practical learning, performance management, knowledge sharing, self-development, training, appreciation and encouragement) in the studied university was in desire conditions.Other findings of the study also show that among organizational factors, components of "organizational culture", "supervisor satisfaction", "organizational dynamics", "working environment conditions", "colleagues", "prestige and brand of the university" and "growth opportunity" were influential on the students' talent development.Also, the results of the data analysis show that among the components of job factors, the component of "person-job fitness" affects the development of students' talents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".