Factores asociados al uso de tácticas políticas en el ascenso laboral: una evaluación desde el contexto de la Universidad de Los Andes, “Núcleo Dr. Pedro Rincón Gutierrez”, Venezuela.
Bibliographic record
Abstract
El presente trabajo tiene por objetivo analizar los factores asociados con el uso de tácticas políticas en el ascenso laboral a fin de evaluarlos desde el contexto de las universidades públicas venezolanas, específicamente en la Universidad de los Andes, núcleo “Dr. Pedro Rincón Gutiérrez” – Táchira. Para ello, a partir de una muestra de 77 profesores se aplicó un cuestionario y se realizaron estudios comparativos a través del Análisis de Varianza de un factor ANOVA usando como variable dependiente el género y distintas dimensiones de las tácticas políticas. Los resultados permitieron determinar que el género no es un factor que determina la utilización de las tácticas políticas y la categoría académica no es una variable determinante para la utilización de las tácticas políticas.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →2 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Survey of factors behind the use of political tactics in academic promotion among university professors in Venezuela; grounded work on the academic workforce and careers, but framed as organizational behavior.
It studies political tactics in university professors' career advancement, making the academic research workforce the object.
Organizational-politics survey of professors’ promotion tactics; object is workplace behaviour, not research practice or the research workforce as such.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".