Labor market transitions in Bolivia during the Covid-19 pandemic
Bibliographic record
Abstract
In a descriptive way, this paper analyzes the transitions in the Bolivian labor market during the Covid19 pandemic to comprehend the patterns and trends of worker transitions across states related to employment, unemployment, temporarily inactive, permanently inactive; also for employed people like salaried, self-employed, unpaid family worker, other and finally for different economic activities. The information used corresponds to the Continuous Employment Survey reported by the National Institute of Statistics of Bolivia which has the virtue of following the same individuals in more than two periods. Labor transition probabilities according to the Markov chain process between the first and third quarter for 2019 and 2020 were obtained and allowed to observe important changes in the Bolivian labor market during the Covid-19 pandemic. Particularly, unemployed persons were in a more vulnerable situation than those who were inactive; however, due to the measures implemented during the peak of the pandemic the probability to flow to inactivity was higher. On the other hand, at first the emergency reduces the possibility for self-employed people to remain in this category; nevertheless, in the following period the self-employed status was an advantageous one.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".