Employment Situation in Latin America and the Caribbean: Labour productivity in Latin America
Bibliographic record
Abstract
Employment Situation in Latin America and the Caribbean is a twice-yearly report prepared jointly by the Economic Development Division of the Economic Commission for Latin America and the Caribbean (ECLAC) and the Office for the Southern Cone of Latin America of the International Labour Organization (ILO). \n \nThe coronavirus disease (COVID-19) pandemic led to an unprecedented crisis in the region’s economies and in its labour markets, where the recovery has been slow, partial and uneven. However, as noted in the first part of this report, there were favourable changes in the main indicators of these markets in the first half of 2022. First, in the second quarter of 2022, the employment rate returned to the level seen before the crisis and the unemployment rate fell 2.8 percentage points compared to the year-earlier period to 7.3%, lower than the pre-pandemic level. Similarly, the participation rate improved, although it is still below the level seen prior to the health crisis. Beyond the difficulties posed by the current labour market situation, the region’s economies face the challenge of reversing the weak growth in productivity and investment registered since the debt crisis.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".