Bibliographic record
Abstract
In this short paper, I explore how my academic journey through Social Informatics (SI) and Science and Technology Studies (STS) has guided me toward the field of Community Informatics (CI), emphasizing the critical distinctions among these disciplines. While SI and STS primarily address the theoretical dimensions of science and technology within various institutional and cultural contexts, CI centers on the practical applications of Information and Communication Technology (ICT) within specific communities. Bringing this interdisciplinary foundation and drawing on a decade of critical ethnographic research, I present the framework of "Mundane Technology," which investigates how marginalized individuals appropriate everyday technologies to navigate and resist systemic oppression. This framework, which was originally introduced in my book “Technology of the Oppressed” (2022, MIT Press), is grounded in a decolonial perspective and enhances our understanding of how ordinary artifacts, processes, and spaces contribute to the agency and aspirations of oppressed communities. This framework also critiques traditional utilitarian approaches in Information Systems and ICT for Development, advocating for a shift towards recognizing the intangible benefits of technology in marginalized contexts. Ultimately, it underscores the importance of incorporating these narratives into CI research, reinforcing democratic values and expanding the scope of technology studies to include the voices and experiences of those historically excluded from power and representation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".