Duración y Probabilidad de Salida del Desempleo: Un estudio para el caso ecuatoriano (2003-2006) con datos de secciones cruzadas repetidas
Bibliographic record
Abstract
The present work studies the effect of unemployment's duration and other individual characteristics on the probability of leaving 1t in the Ecuadorian labor market between February 2003 and January 2006. The results show an inverse relationship between the duration and the probability of leaving unemployment up to the fifth quarter; yet, starting with the sixth quarter the relation becomes direct. Furthermore, 1t is shown that women, people who are married, those with a lower educational level, those with a child at home, and people between 20 and 50 years old constitute the demographic group with the larger probability of leaving unemployment. Finally, because some of these people cease to search for a job, a larger probability of leaving unemployment does not necessarily imply a larger probability of actually getting a job.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".