Quality education for Latin American countries: analysis and contributions from the Policy on Educational Success of Québec
Bibliographic record
Abstract
This paper aimed at presenting recommendations on high-quality education to Latin American countries’ governments by taking the Policy on Educational Success of Québec as Québec’s main official document to achieve quality education. By using the content analysis method, it was investigated the extent to which the Policy on Educational Success of Québec adheres to the Sustainable Development Goal (SDG) number 4 (Quality Education), as well as it was analyzed the concordance of the most significant words from the same policy. Findings demonstrate that the Policy on Educational Success of Québec comprises acts that mostly go towards the promotion of equitable development of the people, to people’s learning, skills, and competences, and to the quality in teaching and in learning processes, and in learning spaces. In contrast, the acts that are less significant in the analysis but equally fundamental for the development of society are related to the minimum conditions to achieve quality education, and to the vocational training to foster quality education. Moreover, it was comprehended that the ensuring of quality education in Latin American countries can immediately be linked to some scopes, like the student, school, service, and education ones. This paper concludes that Latin American countries’ governments can support ensuring quality education for their peoples whether they give special attention to the student, school, service, and education scopes in society.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".