BRAZIL, CANADA AND CHINA: A CROSS-COMPARE STUDY OF ITS EDUCATIONAL INDICATORS - Brasil, Canadá E China: Um Estudo Comparativo De Seus Indicadores Educacionais
Bibliographic record
Abstract
In a globalized world, nations need to follow the development and performance of their peers. Therefore, the study aims to analyze the educatonal indicators in Brazil, Canada and China with the goal to identify the behavior of these indicators as a way to signal benchmarking and learning opportunities. For that, a descriptive quantitative research was performed, through the collection of documentary and bibliographic data from databases worldwide. In order to understand the performance of the educational indicators from the countries, a comparative analysis by regression models via log-linear Quasi-Likelihood method was carried out. The goal was to understand the performance of each indicators in their countries in order to understand their behavior in the 2003-2012 period. The conclusions showed that Canada is a country that has already achieved a high level of development, including its higher education, and that can be considered a reference for other nations seeking to achieve similar progress as Brazil and China itself. In addition, China, a member of BRICS, which is a pair of Brazil, has been able to achieve satisfactory results in the indicators that guide higher education, which highlighted the advantages of conducting a benchmarking of their actions and policies. http://dx.doi.org/10.21714/1679-18272018v16n1.p1-15
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".