Big Data and <i>The Phantom Public</i> : Walter Lippmann and the fallacy of data privacy self-management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 1927, Walter Lippmann published The Phantom Public, denouncing the ‘mystical fallacy of democracy.’ Decrying romantic democratic models that privilege self-governance, he writes: “I have not happened to meet anybody, from a President of the United States to a professor of political science, who came anywhere near to embodying the accepted ideal of the sovereign and omnicompetent citizen.” Almost 90 years later, Lippmann’s pragmatism is as relevant as ever, and should be applied in new contexts where similar self-governance concerns persist. This paper does just that, repurposing Lippmann’s argument in the context of the ongoing debate over the role of the digital citizen in Big Data management. It is argued that proposals by the Federal Trade Commission, the White House and the US Congress, championing failed notice and choice privacy policy, perpetuate a self-governance fallacy comparable to Lippmann’s, referred to here as the fallacy of data privacy self-management. Even if the digital citizen had the faculties and the system for data privacy self-management, the digital citizen has little time for data governance. We desire the freedom to pursue the ends of digital production, without being inhibited by the means. We want privacy, and safety, but cannot complete all that is required for its protection. If it is true that the fallacy of democracy is similar to the fallacy of data privacy self-management, then perhaps the pragmatic solution is representative data management: a combination of non/for-profit digital dossier management via infomediaries that can ensure the protection of personal data, while freeing individuals from what Lippmann referred to as an ‘unattainable ideal.’
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it