Children, Citizenship, and Commons: Insights from Three Case Studies in Lisbon on the 3 C's
Bibliographic record
Abstract
Abstract Listening to children in educational settings is vital for establishing inclusive and equitable environments. This approach recognizes children as active agents and contributors to their education, enabling them to express their needs and participate in decision-making processes. By involving children in educational discourse, pedagogical practices can better align with their interests, resulting in more effective, engaging, and democratic learning experiences. The synergy between Childhood Studies and Educational Sciences underscores the necessity of heeding children’s voices to enhance educational quality and foster active citizenship. This chapter presents the findings of the SMOOTH subproject—RED_Rights, Equity, and Diversity in Educational Contexts. It conducted three case studies in Lisbon, Portugal, involving focus groups with children from diverse educational contexts, involving both formal and non-formal settings, between September and October 2022. These studies aimed to explore diverse dimensions of the educational commons concept, including children’s roles as commoners, commoning practices, and communal aspects related to goods and values within educational and community settings. The findings apprise children’s perspectives as citizens and commoners, highlighting their creativity, self-awareness, interests, and active participation in activities. Additionally, they shed light on emotional and expressive reactions and highlight intersectionality issues within these contexts. This research underscores the vital importance of listening to children, ultimately enhancing educational quality, and promoting active citizenship.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".