Privacy Advocacy from the Inside and the Outside: Implications for the Politics of Personal Data Protection in Networked Societies
Bibliographic record
Abstract
For the most part, privacy and data protection laws arose not through grassroots pressure but through interactions between governmental and business elites in the context of broader international harmonization efforts. Thus, civil society activists have rarely been seen as a client constituency with equivalent weight to governmental and business interests. There is evidence, however, that the privacy advocacy network is becoming more influential in comparative context. In most countries, a network of advocates has emerged with a relatively distinct profile from the “official” data protection authorities. Individual advocates play several conflicting roles and often exist within groups with wider civil liberties, human rights, digital rights, or consumer interests. Those at the center of the privacy advocacy network possess a set of core beliefs about the importance of privacy, and as one passes to the outer edges the issue becomes more and more peripheral. Privacy advocacy is beginning to occur from both the inside, and the outside, representing an important shift in the evolution of privacy protection policy both nationally and internationally, and producing difficult tensions between the two networks.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.097 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".