SITUATION OF SPECIAL EDUCATION IN BRAZIL AND CANADA DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The Covid-19 pandemic has placed the world in a public health emergency since the beginning of the year 2020, posing a challenge to education, especially to Special Education. This study aimed to analyze the situation of special education in Brazil and Canada during the Covid-19 pandemic. The research was part of a postdoctoral project in education. Methodologically, it is an international comparative study in education. It was carried out from April 2020 to November 2021, based on a literature review and official documents. The search was carried out in the Scientific Electronic Library Online (SciELO) and in the Institute of Educational Sciences (ERIC), and 233 articles were found. After analysis and following the inclusion criteria, 217 articles were excluded. In the end, 16 studies were selected. The results show that the more structured the educational system and Special Education, the greater the chances of success. In addition to returning to face-to-face classes earlier, Canada also provided more support for families and students, but it was still insufficient. In both countries, children with disabilities are in a fragile situation owing to social isolation. Aspects such as choice of tools, internet connection quality, user skills, virtual spaces for collective support, school and family communication, strengthening of special education policies and programs, adequate support, among others, led to the success of remote education. This research is intended to contribute to an increase in the number of studies in Brazil and abroad, serving as a basis for the scientific production of other international comparative studies. Keywords: Covid-19, special education, comparative study, literature review, Brazil and Canada
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".