Artificial intelligence, emotional labor, and the quest for sociological and political imagination among low-skilled workers
Bibliographic record
Abstract
Abstract This study examines how generative AI impacts low-skilled workers in their daily professional lives, how it changes the nature of their work, and what, if any, strategies they develop to cope with this new reality. Emphasis is placed on call center agents—an occupational group facing a particularly high automation risk. Drawing on Constructivist Grounded Theory and semistructured interviews in an Austrian call center, we uncover how flawed generative AI tools have increased emotional labor among these workers. This increase is hypothesized to result in agents’ inability to embed their own problems in the larger social context of generative AI’s impact on the labor market, let alone to politicize these problems. They were thus said to lack sociological and political imagination. Our study is the first to link emotional labor with these forms of imagination among low-skilled workers, offering new analytical tools for future research on generative AI’s nuanced effects on the labor market. To empower low-skilled workers, foster their imaginations and address their concerns, we propose several policy recommendations, including targeted education campaigns, enhanced social dialogue, co-determination rights, and tailored upskilling programs. This study thus offers a valuable contribution to scientific research while providing practical implications for policymakers.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".