Identity Commodification: How are Social Identities Strategically Used and Who Benefits?
Bibliographic record
Abstract
In this symposium, we aim to develop the nascent managerial literature surrounding identity capitalism: how are social identities used for social and economic gain? This symposium will explore the various ways in which identities might be commodified, as well as its drivers and consequences. In addition to organizational commodification, some papers investigate self-commodification; how might people use (or feel pressure to use) their own identities for gain? Although there may be negative consequences, we also highlight potential positive outcomes of organizational practices that commodify identities. Organizational Diversity Cases: A Source of – and Solution to – Gender Gaps in Performance? Author: Oriane Georgeac; Boston University Author: Aneeta Rattan; London Business School Diversity Incentives Increase Women’s Aspirations to Lead Author: Erika Kirgios; Author: Edward Chang; Harvard Business School Diversity Dispersion in Team Assignments: Lay Beliefs and Unintended Consequences Author: Adrienne Mishel Kafka; Duke University Author: Anna Li Yan; Duke University Author: Grainne Fitzsimons; Duke University Queerness as a prejudice equalizer… or not? Author: Sa-kiera Tiarra Jolynn Hudson; UC Berkeley, Haas School of Business Author: Kyneshawau Hurd; - An Inductive Examination of Applicant Perceptions and Experiences of Signaling “Diversity Value” Author: Joyce He; University of California Los Angeles Author: Grusha Agarwal; University of Toronto Author: Sonia Kang; University of Toronto
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".