Verified: Self-presentation, identity management, and selfhood in the age of big data
Bibliographic record
Abstract
What new styles of selfhood and self-presentation, forms of social status, and arbiters of “authenticity” are being authorized and propagated in the wake of big data and affective capitalism? How are they functioning, for whom, and to what end? This article takes up these questions via an examination of a sought-after user identity badge, the Twitter verification checkmark, figuring it as both an affective lure that incentivizes specific styles of self-presentation and a disciplinary means through which capitalist logics work to condition and subsume the significance of the millions of forms of self-presentation generated daily. Beneath the promise of democratized access to social status and fame, the business practices of the social platforms in and through which we self-present draw us into privatized strategies of social sorting, identity management, and control. To conclude, the article will posit a new “ideal type” of selfhood for the big data age.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".