Elimination of User-fees in Tertiary Education: A Distributive Analysis for Ecuador
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
This paper offers new evidence and methods for understanding the distributive effect of a universal government policy to eliminate user fees in public universities in Ecuador. The main argument to eliminate user fees in higher education is that it will increase enrollment among the poor. In this regard, eliminating tuition fees is supposed to be a progressive policy. Using several panel data, however, credible evidence exists that eliminating tuition fees has no significant impact on opportunities for tertiary education. In addition, the policy becomes regressive two years after its implementation. Results, however, are sensitive to the welfare indicator used, i.e., either assets index or income poverty. In any case, results show that, at a minimum, the policy had non-progressive effects.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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