Through the eyes of the beholder: University student leaders’ understanding of citizenship
Bibliographic record
Abstract
Abstract This article explores the concept of citizenship based on the experience of student leaders from a mid-sized university in western Canada. Five student leaders participated in semi-structured individual interviews to explore their experience with, and understanding of citizenship. Interviews concentrated on personal view points and definitions of citizenship, explored whether or not there are good and great citizens, and the role universities play in fostering strong citizenship amongst its student body. The measurement of citizenship and opportunities to foster citizenship were also explored. Qualitative content analysis revealed five themes, including political participation, social citizenship/solidarity, engagement, transformative action and autonomy. Citizenship, while highly valued by this population, also appears to be impossible to measure. If post-secondary institutions are aiming to create better citizens, more work needs to be done to create a common understanding of the intended outcome. Based on these findings, a new potential model of citizenship is proposed, in line with the work of Dalton and others who emphasize a shift towards personal involvement over traditional political engagement. Further, these results suggest that students could benefit from understanding themselves as political agents, capable of inculcating change within the university context and beyond.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".