A memória e a história do Centro de Recém-Chegados das escolas públicas de Mississauga/ON, na trajetória de reconhecimento social de imigrantes no Canadá
Bibliographic record
Abstract
The research consists of an institutional study of memory and history of a program in the city of Mississauga/ON, Canada, as a public policy of reception and social recognition for immigrants, through the lens of Axel Honneth's theory. The general objective is to understand the social memory and institutional history of the Newcomer Reception and Assessment Centre financed by public management and executed through Catholic public schools in the city of Mississauga/Ontario, in Canada, based the narratives of a Brazilian immigrant group, about the inclusion, belonging and social recognition theorized by Axel Honneth, from 2019 to 2022. The social relevance of this research is to recognize the importance of public management and government interference involves in issues involving the eradication of historical inequalities and marginalization arising from immigration. The method is qualitative-descriptive and uses semi-structured interview with a manager and immigrants as a tool for data collection, using content analysis based on memorial narratives. The belonging and social recognition of Brazilian immigrants who arrived in Canada between 2019 and 2022 will be an in-depth theme in this study. It is inferred that there is a great effort on Canada in the creation and maintenance of policies adopted to assist immigrants in their integration into the new territory in the inference that contributes to Social Recognition, both in the individual and in the public spheres.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".