Cook, Sharon Anne (September, 2004). Learning to be a Full Canadian Citizen: Youth, Elections, and Ignorance. Canadian Issues Magazine. Association for Canadian Studies. Learning to be a Full Canadian Citizen: Youth, Elections and Ignorance
Bibliographic record
Abstract
he discouragingly low percentage of Canadians who chose to vote in the just-past federal election campaign – the lowest at 60.5 percent since Canada’s first federal election held 137 years ago – has preoccupied political observers throughout the summer. Even more worrisome, however, is the rate by voter age: in the 2004 election, 80 percent of those aged 58 to 67 voted, 66 percent of those between 38 and 47, while only 22 percent of 18 to 20 year-olds did so.i Clearly, the civic disengagement of newly-minted voters, those fresh from mandated civics courses in highschool, is greatest of any group in the population. Behind this specific anxiety related to voter turn-out, however, is a considerable educational literature which explores the roots of youth apathy to broader questions of civic commitment and involvement,ii especially as these relate to the teaching of Social Studies and History,iii the difficulties of engaging students ’ interest in formal civics instruction,iv and the far-ranging implications for civic culture of youths ’ disinterest, and even their rejection of
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".