Participatory design and web 2.0: the case of PIPWatch, the collaborative privacy toolbar
Bibliographic record
Abstract
We discuss the distinctive opportunities and challenges of adopting a PD approach to the development of 'Web 2.0' applications. Web-based services pose significant difficulties in interacting effectively with user groups in terms of traditional PD methods. However there are some quite popular 'peer-production' services which have been successful in overcoming such challenges and thereby offer useful insights into participatory approaches for developing applications that depend on the ongoing voluntary contributions by groups of physically dispersed individuals. These are illustrated through a reflective account of the iterative development of PIPWatch, a Firefox extension that enables web users to monitor the privacy policies and practices of the websites they visit, using data contributed by previous visitors and site privacy officers.
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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 | 0.066 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".