Co-creation of educational commons spaces to reverse inequalities: project SMOOTH and the Children's Club
Bibliographic record
Abstract
This article presents an action-research project from the EU-funded SMOOTH project, which focuses on the potential of Educational Commons to address educational inequalities. The project adopts an emergent paradigm that views spaces for collaboration, content co-creation, socialization, governance, and play as catalysts for reversing inequalities. The action-research, conducted in a disadvantaged non-formal education setting in northern Portugal, involved children aged 8-10 years old. Over a span of 10 months, the innovative action-research program aimed to achieve several objectives: (1) reversing inequalities faced by vulnerable social groups, (2) strengthening inter-cultural and inter-generational dialogue and social integration, (3) developing essential social and personal skills, and (4) creating smooth spaces of democratic citizenship based on equality, collaboration, sharing, and caring. By understanding the tensions and conflicts that emerge in children's everyday situations, the project sought to build and foster community through embracing differences. This article analyzes the characteristics, behaviors, challenges, and strengths observed during the 30 sessions. The results provide insights into the dimensions of Children as commoners, in terms of sharing and care, cooperation and collective creativity and active citizenship. This research contributes to the exploration of Educational Commons as a means to promote equity and transform educational contexts.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".