Students’ civic engagement in Ukraine and Canada: a comparative analysis
Bibliographic record
Abstract
In this article, the authors have carried out a comparative analysis of students’ civic engagement in Ukraine and Canada. They have surveyed the students at Pavlo Tychyna Uman State Pedagogical University and compared the findings with the results of a study done by the Canadian researcher Catherine Broom at British Columbia University. Based on the research findings, the authors have identified Ukrainian students’ personal political and civic experience levels and compared them with the Canadian results. The study reveals Ukrainian students’ attitudes towards political and civic participation, democracy, the government in general and in comparison with Canadian data. The research results have identified the following key factors that influence Ukrainian students’ civic activity: students’ free time activities their attitudes and beliefs. According to the survey, gender, religious involvement, personality type, and family’s political involvement do not directly influence the students’ civic engagement. The survey has not reported any influence of school social study courses on civic engagement, stressing the importance of real-life experiences that result in attitudes and intrinsic motivation. The authors have also revealed examples of motivations and barriers for youth civic involvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".