Classification of competences according to their difficulty in detecting human talent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it