A framework to explore lifelong learning: the case of the civic education of civics teachers
Bibliographic record
Abstract
This study investigates learning about civics and citizenship throughout individuals' lives (lifelong) and across various pedagogical settings (lifewide). A basic hypothesis is that civics teachers, among all social actors, are particularly well positioned for engaging in this type of introspective exercise because they are both familiar with civics and politics and also with teaching and learning processes. The lifelong civic learning of civics teachers was examined in the different settings in which they acquire their knowledge, values, skills and ideological frameworks, and to understand the relative weight of each one in their overall learning process. This study also coincides with the implementation of a new provincial civics course for grade 10 students in Ontario, Canada during the 2000–1 school year. This case study consists of interviews with 15 social studies teachers who have taught the new civics course in Ontario. One of the clearest findings of the study is the powerful influence of the experience of teaching and of early family socialization on the acquisition of civic knowledge, skills and values, and on the development of political beliefs. Civic engagement and political participation were also considered an important source of civic learning, particularly in relation to the acquisition of civic and political skills. This is a finding that deserves further exploration, because our understanding of social movement learning remains limited. The findings suggest the promotion of lifelong citizenship learning entails the creation and nurturing of inclusive democratic spaces that have particularly high civic educational potential.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".