Personal Identity Economics: Facebook and the Distortion of Identity Politics
Bibliographic record
Abstract
This article examines Facebook’s role in the treatment of marginalized identity as currency. Recent examples of solidarity statements and corporate social responsibility rhetoric treat disenfranchised racial and gender identities as value-added competitive market quantities to boost brands. This trend also incentivizes marginalized actors to capitalize on their own disenfranchisement in pursuit of visibility and career advancement. The resulting identity politicking replaces communal care, grassroots social ties, solidarity, and interdependence with isolating market competition. This article diverges from scholars who trouble the differential value of identity—by troubling the valuation of identity itself. Facebook normalizes identity as private property in what I call a transition from identity politics to “personal identity economics.” I coin this concept and break it down into the following four factors: (1) The optimization of difference beginning in the 1970s, (2) Facebook’s algorithmic invasion of market logic into intimate aspects of life starting in the mid 2000s, (3) Ads Manager’s economization of identity into legible economic units, and (4) neoliberal corporate social responsibility rhetoric of “social good” as a profitable asset.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".