Bibliographic record
Abstract
To suggest that privacy is dead is not to revel in or encourage its demise, nor even to claim that it is not a desirable outcome, right, or valued policy. Rather, what this paper suggests is that in certain circumstances (increasingly on social media platforms) the privacy of users now stands in direct opposition to the stated goals and logic of the technology in question. One need not give up certain goals of privacy to recognize that business models of online companies like Facebook and Google are now entirely predicated upon the act of going public--there would be no Google search engine or Facebook social networking platform without the content, information, and demographic profiles uploaded, revised, updated, and shared by billions of users worldwide. This paper then offers some initial thoughts on a theory of publicity, of going public in the social media age. If social media platforms are governed by ubiquitous surveillance and continuous uploading and sharing of personal information, opinions, habits, and routines, then privacy would seem only to be a hindrance to these processes. To ignore such clear mission statements, coupled with repetitive attempts to undermine, display, and obfuscate so-called privacy settings, would seem disingenuous at best, and willfully blind at worst. These online platforms profit from publicity and suffer from stringent privacy protocols--their whole raison d’être is to learn as much as possible about users in order to aggregate and then sell such profiled and clustered information to advertisers and marketers. Can we really conclude that such businesses violate users’ privacy when their platforms are in the first and last instance wired for ubiquitous publicity? Or more to the point, do privacy-based perspectives provide an adequate framework for understanding users’ relationships with social media platforms and their parent companies?
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".