Deep Trust in the future of Community Informatics
Bibliographic record
Abstract
Engaging in community-based ICT projects, whether for research, design, or implementation purposes, often requires a long-term engagement among practitioners, researchers and community members. In this paper, we discuss how these projects are fundamentally shaped and reshaped by the trust building process, through which 'relations' with a community become deeper 'relationships'. The discussion is based on our experiences in two separated field sites: a Seniors Community Center in Northern Italy, where we established a 3-years long participatory research and design project; and an 8-month ethnography of Community Technology Centers in three marginalized favelas of Vitória, Brazil, where we have explored ICT use by local residents. We identify the difficult challenges in the process of developing trust relationships, commonalities between the two different contexts, and discuss the role of “deep trust” relationships in the future of CI.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".