Bibliographic record
Abstract
T he historicized context within which today’s youth activists come to their practices consists of almost impossibly complex layers of personal and family histories, regional and national histories, global flows of migration and trade, and multiple other influences and effects. Thus, the task this chapter sets out to achieve is, in many ways, an impossible one: to understand the contemporary construction of Citizen Youth through a history of the present. Nonetheless, this chapter aims to rise to the challenge posed by Wacquant, of historicizing “everything having to do with democracy,” including, in this case, the constitution of those individuals who make up a democracy, its citizens. The slice of history the chapter draws on for this task is that of citizenship education as it has evolved within the particular liberal democratic state of Canada. In doing so, I am attempting to answer the question that Hava Gordon (2010) poses within the U.S. context: “[What] role [does] schooling [play] in constructing youth as citizens-in-the-making rather than as actualized political forces in their own right?” (60). I tackle this question through an interpretive tool adapted from Paul Ricoeur’s phenomenological notion of the “detour.” These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".