Volunteering the Valley: Designing Technology for the Common Good in the San Francisco Bay Area
Bibliographic record
Abstract
How can digital technologies be designed for good rather than harm? Dozens of civic organizations under the “tech for good” banner have emerged in recent years to address exactly this question. Although these organizations have commendable goals, many scholars have criticized them for naively believing that technologies can solve complex social problems. However, we do not yet have empirical data on how they are, in practice, working to address local social problems. This study investigates one particular effort to design digital technologies for the common good: civic technology. Civic technology organizations are made up of technologists—employed or seeking employment in the high-tech industry—who volunteer in their spare time to build digital technologies to be used by municipal employees and local residents. Drawing on participant observation and interviews with civic technologists in the San Francisco Bay Area, I argue that civic technologists’ efforts end up being less about serving local residents and more about proving that, despite current critiques, the Big Tech industry can still ‘save the world.’ To capture the complex dynamics which lead volunteers to repair their investment in the Big Tech industry even as they critique it, I develop the concept of the “spirit of civic technology,” which is an ethos comprised of value judgments about what makes a ‘good’ technology, technologist, project, and organization, and which are exported from high-tech workplaces into civic organizations. I conclude the spirit of civic technology leads volunteers to inadvertently reinforce the epistemic, economic, and cultural power of Big Tech firms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".