Citizenship Education in Multicultural Society: Teachers’ Practices
Bibliographic record
Abstract
International literature indicates a decrease in participation (Schulz & Sibberns, 2004; Birzea et al., 2005) in western societies where legal frameworks are challenged by sociopolitical and cultural changes like migration, neoliberalism and globalization (Benhabib, 2002). These changes also affect the identity construction processes of new generations, as participation, belonging, and citizenship are rooted in identity formation ( Bell, 1999 ) and instituted through family and school. In contemporary democratic societies schools must offer a new model of citizenship with multiple possible memberships. “Whilst every individual, to a certain extent, is a product of his or her heritage and social background, in contemporary modern democracies everyone can enrich his or her own identity by integrating different cultural affiliations” ( Council of Europe, 2008 , 18).This paper presents exploratory research conducted to verify if and how teachers and institutions engage in education for democratic citizenship from an intercultural perspective. We conducted qualitative conversational interviews with 47 teachers (8 preschool, 16 primary, 15 middle school and 8 secondary). Open-ended questions on their missions and what they do in class to promote citizenship education were employed to understand their beliefs about citizenship education and their practices to promote a democratic intercultural habitus among students. Some teachers had ambiguous or assimilationist conceptions of citizenship education, and others based it on ethics and interculturalism for an inclusive concept of citizenship. Teachers’ descriptions of practical actions fell into seven major categories: belonging, recognizing differences, listening to others, managing conflicts, participation, making rules together, and building communities. Some of their good practices are not yet instilled institutionally, so teachers trying to foster a democratic habitus feel isolated within their institutions and wider society. Transferring universal declarations and ethics into daily practice cannot be done by teachers alone, but requires the examples and actions of adults in society at large. Also, teachers need specific training interventions to orient this work.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".