How is social inequality maintained in the Global South? Critiquing the concept of dirty work
Bibliographic record
Abstract
Extant research on dirty work—occupations involving physical, social, or moral taint, which affect worker identities—has been read primarily through the lens of social identity theory (SIT). There are two notable shortcomings that emerge as a consequence of dirty work being too heavily reliant upon the precepts of SIT, which we seek to remedy in this article: (1) the overemphasis on the symbolic to the detriment of the material has led to false optimism regarding the ability for subjects doing dirty work to exercise agency in constructing their own sense of selves, and (2) the failure to substantively account for the role of identity differences suggests that empirical research on the phenomenon is devoid of proper historical and cultural contextualization. Drawing on a qualitive study on low-caste toilet cleaners in Pakistan, our findings were largely incongruous with the scholarly conceptualization of dirty work that has been propagated to date. We explicate the embedded role of power and context in dirty work, which are not adequately considered using SIT alone. Repudiating the overly romanticized version of the concept, we argue that SIT’s account of dirty work ought to be complemented by status construction theory going forward.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".