A Comparison of the Portrayal of Visible Minorities in Textbooks in Canada and China
Bibliographic record
Abstract
Canada and China are both multiethnic countries and have articulated a strong commitment to multicultural education. However, in the process of curricula control, decision makers, drawn from the mainstream culture, develop, implement, and interpret the formal curriculum. Consequently, the ethnic content included in the mainstream curriculum could be biased, fragmented, or with important omissions. This paper evaluates the portrayal of selected visible minorities in some currently used social studies (history) textbooks in Alberta and China, to reveal how knowledge of ethnic cultures is filtered through the dominant perspectives and to explore ways to educate all students to be responsible citizens. The analysis and discussions are situated within the conceptual framework of identity construction and critical pedagogy. Data are collected by observation, informal conversations and textbook analysis. Le Canada et la Chine sont tous les deux des pays multiethniques et ont formulé un engagement ferme pour une éducation multiculturelle. Cependant, durant le processus de contrôle des curricula, le curriculum formel a été développé, établi, et interprété par des responsables qui sont influencés par la culture du groupe dominant. Par conséquent, le contenu ethnique du curriculum pourrait être biaisé, fragmenté , ou marqué d’omissions importantes. L’intention de cet article est d’évaluer la représentation des minorités visibles, choisie dans quelques livres scolaires en usage dans les sciences humaines (histoire) en Alberta et en Chine, pour révéler comment la connaissance des cultures ethniques a été filtrée à travers les perspectives dominantes et pour explorer des directions afin d’éduquer tous les élèves à devenir des citoyens responsables. L’analyse et les discussions sont situées dans le cadre conceptuel de la construction d’identité et de la pédagogie critique. Les données ont été recueillies par des observations, des conversations informelles et de l’analyse des livres scolaires.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".