Trapped in the Digital Divide: The Distributive Paradigm in Community Informatics
Bibliographic record
Abstract
This paper argues that over-reliance on a distributive paradigm in community informatics practice has restricted the scope of the high tech equity agenda. Drawing on five years of participatory action research with low-income women in upstate New York, I explore the ways in which distributive understandings of technology and inequality obscure the day-to-day interactions these women have with ICT and overlook their justified critical ambivalence towards technology. Finally, I offer unique insights and powerful strategies of resistance suggested by my research collaborators in a drawing exercise intended to elicit alternative articulations of digital equity. If we begin from their points of view, the problems and solutions that can and should be entertained in our scholarship and practice look quite different.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.126 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".