Bibliographic record
Abstract
Discrimination remains integral to understanding both how inequality is produced and how it can be remedied in employment settings. Yet like many sociological concepts, the notion of discrimination involves an uneasy mapping of theory to practice. Traditional conceptualizations of discrimination as differential treatment are ill-fitted to the structural and relational nature of much discrimination in the contemporary era. The disparate impact doctrine, which recognizes policies or practices that systematically disadvantage protected groups, picks up some of the theoretical slack, but offers little in the way of conceptualizing individuals’ complex and entangled experiences with inequality at work. In this article, I provide a conceptual reorganization of theories of discrimination, underscoring recent calls to move beyond the confines of the current disparate treatment and disparate impact binary by recognizing the structurally and culturally embedded nature of bias and discrimination. Drawing on recent sociological research as well as my own analysis of legal records and interviews with plaintiffs involved in high-profile sex and race lawsuits settled in the past decade, I illustrate how differential treatment emerges in the context of and enabled by systems of vulnerability and privilege, workplace culture, and compositional asymmetries. I conclude with a discussion of the implications of this framework for antidiscrimination enforcement efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".