Digital Transformation and Dirty Work
Bibliographic record
Abstract
This symposium examines how digital transformation reshapes “dirty work,” occupations marked by physical, social, or moral stigma, as they adapt to digital spaces. Traditionally marginalized dirty work has been stigmatized by associations with “unclean” objects, subservient roles, or morally contentious activities. Digital platforms and AI technologies now alter this landscape across individual, occupational, and industry levels. For instance, regulated digital platforms may reduce stigma in sex work, while virtual services reshape perceptions in industries like ride-hailing. Additionally, digital spaces create new stigmatized roles, such as content moderation and data labeling, which expose workers to harmful content, mirroring traditional “dirty” occupational activities. Our symposium will examine three dimensions: the transformation of stigmatized roles through digitalization, the shifting social evaluations of dirty work via online spaces, and the emergence of novel digital “dirty” occupations. This discussion aims to illuminate the processes by which digitalization may destigmatize, redefine, or even reinforce the boundaries of dirty work, emphasizing the pivotal role of digital spaces in shaping public opinion and occupational identity. Through this lens, we seek to enhance understanding of digital transformation as both a driver of change and a platform for societal renegotiation of stigmatized labor.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".