Adult Learning for Active Citizenship: Exploring Learning Pathways around Citizenship and Participation in Community Organizations and Governance
Bibliographic record
Abstract
Understanding what motivates adult to engage in various learning endeavours across the lifespan often involves tracing multiple complicated and interconnected factors. Both formal and informal educational contexts determine how individuals will be politically involved through different stages in their lives. In a current study on lifelong learning, citizenship, and participation in community-based organizations in Canada, the possibilities and challenges of developing a more networked approach towards governance to support an active and engaged citizenry is explored. This study is funded by the Canadian Council on Learning (CCL) and builds on previously completed research around women’s lifelong learning trajectories in adult and higher education in Canada funded by the Social Science and Humanities Research Council of Canada, as well as a previous CCL study on life histories of women as active citizens. The findings reveal a complex meshwork of factors that shape decisions around participation in both formal and informal learning contexts to become “active citizens”. Differing perspectives are explored around the role of government, community-based organizations (CBO’s), and volunteer participation as these relate to governance. Critical discourses in citizenship are used to explore how localized factors are often influenced by the effects of globalization and neoliberalism.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".