“What Is the Alternative Then?” Affective Challenges in Citizenship Education for Sustainable Intercultural Societies
Bibliographic record
Abstract
This study explores the ways in which Canadian teachers construe the complexity of citizenship education, utilizing the key concepts of affect and difficult knowledge to examine the challenges to democratic citizenship within increasingly diverse intercultural societies. The findings from the semi-structured qualitative interviews with six social studies teachers reveal how affective dynamics emerge prominently as they grapple with tensions between idealized conceptions of multi- and intercultural citizenship and ongoing challenging issues (e.g., social inequality and exclusion). The findings reveal a problematic pattern of antinomical attitudes as a dilemma—where teachers outwardly acknowledge ethical obligations to address ongoing injustices while simultaneously resisting the deeper structural changes necessary for sustainable intercultural societies. In doing so, this study illuminates how affective dynamics function as an onto-epistemological power behind social production that shapes our cognitive rational deliberations on citizenship and undergirding ideology(ies). These findings offer critical insights into the ethical challenges of education for sustainable intercultural societies amid a global landscape where extreme nationalism intertwines with neoliberal market-driven imperatives. This study thus provides implications for critical pedagogical approaches for citizenship that embrace myriad affective dynamics to create transformative learning spaces for citizenship education, particularly in addressing systemic inequalities. Such approaches could pave pathways toward acts of citizenship to disrupt already defined orders, practices, and statuses so integrally as to make claims for justice.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".