HIGHER EDUCATION SYSTEMS AND THE CHARGING OF TUITION FEES: PERSPECTIVES AND PERCEPTIONS IN BRAZIL, CANADA AND CHINA
Bibliographic record
Abstract
This study aims to understand the perceptions of the charging of tuition fees in Canada and China and the perspectives of this practice in Brazil, which is a country where society requires access to quality higher education, but which faces large budgetary constraints on its public funding. To understand these questions, a qualitative exploratory multi-case study was conducted with government officials, specialists in higher education, and student leadership from all three countries, from 13 semi-structured interviews and 27 responses to an unstructured questionnaire, in order to clarify a subject still paradigmatic in the scope of Brazilian public higher education. In conclusion to this analysis, and considering the benefits provided by the Canadian and Chinese participants, it is suggested to Brazil to carry out a reflection on this subject. This reflection is necessary, especially if the need to increase the financial support to these institutions is proven and this cannot be done because of the financial availability. This initiative would aim to boost public higher education institutions through greater financial availability and democratize access and permanence, as it would have more resources to invest in assisting students from underrepresented groups participating in the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".