Community design: a collaborative approach for social integration
Bibliographic record
Abstract
This paper describes a successful case of collaboration among a south-north project team, a Community Multimedia Centre (CMC), and community beneficiaries, for the design and implementation of a small-scale project to improve CMC services for the local community of Quelimane, in Mozambique. The project is part of RE-ACT, a broader scale research and development project which aimed to investigate the social meanings and understandings attributed by different stakeholders to Mozambican CMCs, and use these insights to co-design and implement CMC services with inherent relevance for the local communities. The case reported is considered the most successful of nine action research and co-design projects implemented through RE-ACT. The services designed for the Quelimane CMC can be considered a success not because of perfect alignment with initial goals but rather due to responsiveness and flexibility in the implementation approach: activities and goals were constantly revised by teams to cope with emerging challenges, while at the same time keeping a clear orientation towards set targets. The paper discusses five underlying factors thought to heighten the chances of success of action research and co-design projects involving local communities, ranging from local proactivity and initiative, to commitment to project success, and high perceived self-efficacy of local stakeholders.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".