The Ecuadorian Amazon: A Data Analysis of Social and Educational Characteristics of the Population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study describes the social and educational characteristics of the Ecuadorian Amazon population. For this purpose, the data obtained from the National Survey of Employment, Unemployment and Underemployment of 2014 was used in this research. A descriptive statistical analysis presents the frequency, the percentages and the graphs of the variables related to the area in which people live, gender, age, ethnic self-identification, language spoken, marital status and level of instruction. Other variables are the use of computer and internet, place of birth, reason why they live in the Amazon region, type of activity or inactivity, how do they feel in their jobs, and groups of occupation. Also, a factorial analysis was used to show the main and most important criteria of differentiation and the the clusters of people with similar characteristics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 it