How Can Be Academic Talent Measured During Higher Education Studies? - An Exploratory Study
Bibliographic record
Abstract
Many articles claim that talent management is a very important aspect of higher education. Despite of this, the studies, which investigate this topic empirically, are very rare. The Hungarian higher education talent management focuses mainly on academic-, scientific aspect of talent. So, the main purpose of talent-management is the academic reinforcement, namely, growing up a new generation of scientist/university teachers. The talent management in higher education can be imagined as a bridge between formal school studies and scientific career. In this study, I search answer for the (research) question: how should academic talent be measured during higher education studies? Moreover, does it have any sense to identify the academic talents during even their higher education studies? The research is based on opinion of 170 university teachers who supervised talented students during a young-researcher competition. The method was questionnaire-method. The questions gathered round two main topics: (1) identifying of talented students and cooperation with talented students; (2) own career of supervisor university teachers. The results had been analyzed with descriptive statistics which show the mostly chosen talent-identifying methods and features of talented students. The open-ended questions had been content-analyzed. The data of university-teacher’s career had been analyzed with mathematical statistical tests (ANOVAs, Two-sample T tests, correlations) where the dependent variable was the number of publication (as indicator of the scientific performance). The results may suggest conceptions for talent-programs (honor programs) based on academic talent; for doctoral schools, and for any other institutes who works with career entrant scientist. The scientific reinforcement would be more effective if scientific programs/scholarships/PhD-programs used professional methods during selection process, instead of subjective choices, based on CV and motivation letter.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".