Bibliographic record
Abstract
This paper analyzes the causes and consequences of the percentage of the youth population that is not working, in school or in traning, Neets, in Guatemala. The study rests on the estimation of regression equations that explain the percentage of Neet population in terms of variables associated with the labor market; other set of equations were estimated to assess the role that Neets have in the Guatemalan economy. The data employed was taken from the World Bank’s World Development Indicators. The results indicate that the percentage of female and male Neets decrease as the credit to the private sector increase; said percentage increases with the increase of the deficit in the trade balance and with the increase in youth unemployment. Another result is that the Neet population exert negative impacts on the employment to population ratio and on the rate of economic growth. These results are augmented by the analisis of the relationships existing between the percentages of Neets and economic growth, the number of homicides and the number of persons that are incarcerated using a cross section of 2010 data from 13 Latin American countries. The results presented in the paper should motivate policy makers in Guatemala and other countries to design and implement policies geared towards preventing that youth become Neets.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".