(Re)creating citizenship: Saskatchewan high school students’ understandings of the ‘good’ citizen
Bibliographic record
Abstract
Citizenship education is of central importance in curriculum and schooling, as evidenced by the proliferation of research and writing in the area over the last 20 years. Building on existing citizenship literature, this paper discusses one aspect of a larger project exploring the ways in which citizenship is discursively produced in officially mandated school curriculum and the ways in which students themselves understand and take up narratives of ‘good’ citizenship in light of their diverse experiences and social locations. Using an image-based approach to research, students visually represented and then discussed with researchers their perceptions of good citizenship. What became apparent through the analysis of images and focus group transcripts was the ease with which students, regardless of their social locations, reproduced commonsense narratives of ‘good’ citizenship, including socially sanctioned concern for the environment, a sense of nationalism and national pride, respect for relationships and a communal ethos, and the official discourse of multiculturalism. Missing from students’ understandings of ‘good’ citizenship was any kind of social analysis, suggesting that they largely accepted citizenship as universally realized and experienced by individuals.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".