Classification of competences according to their difficulty in detecting human talent
Bibliographic record
Abstract
The research seeks to determine the classification of competencies according to their difficulty in detecting human talent at the Hotel Campestre Santa Catalina of the Municipality of San Gil - Santander. With a quantitative method, descriptive type, field design, cross-sectional and non-experimental, using twelve 12 subjects as observation units. The survey technique was used, and a 15-item structured questionnaire was used as an instrument, validated by the judgment of five (5) experts, with a reliability of (0.82) according to Cronbach's Alpha coefficient, being highly reliable. Data analysis was performed by coding and tabulation, with the IBM SPSS Statistics V.22 program. The results show that the total arithmetic mean is 3.99 percentage points, reflecting the sum of trends 74% positive, 19% neutral and 7% negative, categorizing the variable as present in the human management model by competencies of the object of study. It is concluded that these findings present a positive trend in human talent processes, stimulating the participation of collaborators to integrate into the organization's strategies and optimize the resources in the jobs, given by the specific competencies of the collaborators.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".