An Analysis of Key Aspects of the Illiterate Ecuadorian Population Aged 15 Years and Older
Bibliographic record
Abstract
This article aims to provide an analysis of several aspects that comprise the profile of the Ecuadorian population aged 15 years old and over who do not know how to read and write. The approach, employed in this research study, focuses on a descriptive analysis and a multidimensional statistical analysis. Available data from the National Survey of Employment, Unemployment and Underemployment of Ecuador (ENEMDU), conducted by the National Institute of Statistics and Censuses (INEC) in 2016, was utilized as the main source of information in the study. One of the previous surveys, conducted in the country in 2006, suggested that the equivalent percentage of the illiterate population was 8.63%; therefore, these data indicate that the population under examination has decreased considerably in the last ten years. This means that a highly important enhancement in terms of literacy has occurred over this period of time in Ecuador. The study results, drawn from the descriptive analysis and the hierarchical analysis carried out as part of this research, may be key at the moment of creating policies, educational campaings and nationwide, special programs focused on helping reduce and eliminate illiteracy across the nation in the upcoming years.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".