Determinantes de la temporalidad en el mercado laboral ecuatoriano // Determinants of Temporality in Ecuadorian Labor Market
Bibliographic record
Abstract
Las diferencias que existen dentro del mercado laboral ecuatoriano, en las diferentes ramas de actividad, evidencian los niveles de formación que los trabajadores deben tener para evitar efectos negativos. Por tal motivo, en este estudio se analiza el papel que las características personales, laborales y geográficas desempeñan en la probabilidad de obtener un contrato temporal frente a uno indefinido. El análisis es realizado por rama de actividad, para determinar qué características posibilitarán la existencia del contrato temporal en cada una de ellas. Para lograr este objetivo, se estiman modelos de regresión logística utilizando los datos de la Encuesta de Empleo, Desempleo y Subempleo desde el 2º trimestre del año 2007 al 2º trimestre del año 2010, elaborada por el Instituto Nacional de Estadística y Censos (INEC). || The differences that exist inside the labor Ecuadorian market, in the different branches of activity, demonstrate the training levels that the workers must have to avoid negative effects. However, in this study, the role played by individual, jobs and residence characteristics are analyzed on the probability of having a fixed-term employment versus permanent employment. It is analyzed concretely by branch of activity, to check which are the characteristics that would make it possible the existence of the fixed-term employment in each of them. To achieve this aim, there are estimated models of logistic regression using the information of the Survey of Employment, Unemployment and Underemployment from 2nd quarter of 2007 to the 2nd quarter of 2010, elaborated by the National Institute of Statistics and Censuses (INEC).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".