VENDO A INCLUSÃO ESCOLAR PELAS LENTES DA DIVERSIDADE CULTURAL: UMA ANÁLISE DE ATIVIDADES DE ENSINO-APRENDIZAGEM EM ESTUDOS SOCIAIS NO ENSINO FUNDAMENTAL
Bibliographic record
Abstract
In Quebec schools, cultural diversity is addressed, among other things, by the Broad Area of Learning entitled “Citizenship and Living Together,” whose educational aim is to enable students to “take part in the democratic life of the classroom or the school and develop an attitude of openness to the world and respect for diversity” (Gouvernement du Québec, 2001, p. 50). However, fulfilling this aim requires subject-specific learning in school, particularly within social studies (history, geography, and citizenship education) (Gouvernement du Québec, 2001). Yet teachers greatly struggle to understand the Broad Areas of Learning (BAL) and to take them into account in their everyday practices (Conseil supérieur de l’éducation, 2007), and no concrete means for addressing BAL on an everyday basis seems to be officially set forth for social studies instruction. This article proposes a preliminary reflection on the issue of cultural diversity in schools based on a consideration of cultural diversity within the Quebec school curriculum and instructional ways of addressing this diversity. The article’s purpose is to outline a conceptual framework that will help devise a model of the instructional treatment of diversity within social studies education at the elementary level and, subsequently, to analyze prescribed (official discourse and textbooks), reported (teachers’ own discourse on their planned and actual practices) and actual (practices implemented by teachers) teaching practices within the same subject.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".