Auto-biography: On the Immanent Commodification of Personal Information
Bibliographic record
Abstract
In the last years, a series of automated self-representational social media sites have emerged that shed light on the information ethics associated with participation in Web 2.0. Sites like Zoominfo.com, Pipl.com, 123People.com and Yasni.com not only continually mine and aggregate personal information and biographic data from the (deep) web and beyond to automatically represent the lives of people, but they also engage algorithmic networking logics to represent connections between them; capturing not only who people are, but whom they are connected to. Indeed, these processes of ‘auto-biography’ are ‘secret’ ones that for the most part escape the user’s attention. This article explores how these sites of auto-biography reveal the complexities of the political economy of Web 2.0, as well as implicate an ethics of exposure concerning how these processes at once participate in the erosion of privacy, and at the same time, in the reinforcement of commodification and surveillance regimes.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".