From Exploration to Design: Aligning Intentionality in Community Informatics Projects
Bibliographic record
Abstract
This article focuses on a particular aspect of research and design processes in community-based projects: the transition from exploratory stages, concerned with knowledge production, to design stages, in which goals for action-taking are formulated and desired directions for change are envisioned. This paper offers a reflection about the methodological processes that underpin this transition, in response to the questions: How are design goals formulated in community informatics interventions that rely on data-intensive exploratory methodologies, and what factors and dynamics shape them? Guided by these questions, we shed light on various issues related to this transition by recounting and analysing cases taken from field experiences within three different community projects in Syria, Brazil and Mozambique. The article proposes that the transition is associated with shifts in intentionality, which are elusive and hard to grasp, particularly in participatory approaches. Three analytical categories are put forward to illuminate the dynamics of intentionality shifts along the continuum of transitioning from exploration to design. Reflections based on the empirical cases are contributed.
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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 | 0.078 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".