The Protestant Ethic and the Spirit of Facebook: Updating <i>Identity Economics</i>
Bibliographic record
Abstract
Scholars and news media generally name Facebook’s two central problems: that its data collection practices are a threat to user privacy, and that stricter regulations are required to prevent “bad actor” from spreading hate and disinformation. However separating these two concerns—personal data collection and bad actors—overlooks the way that one generates the other. First, this article builds on critical race scholarship to examine how identity politics are historically distorted and commodified into profitable vigilance and intolerance, in what I call a transition from identity politics, to personal identity economics. Facebook’s Ad Manager, for example, reveals how personal identities are itemized as advertising assets, which are cultivated through deeper, more trenchant identity politics. Second, this article theorizes about what makes such staunch, intolerant identity politics addictive. Drawing on Max Weber’s theories of the Protestant Ethic, this article explores how Facebook activism thrives on deep-rooted Christian paradigms of dogma, virtue, redemption, and piety. As dogmatic personal identity economics spread across the globe, they testify to how Facebook’s business model manufactures bad actors.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".