Goffmanian “Cooling” in Technology-Mediated Frontline Enforcement Work
Bibliographic record
Abstract
Frontline workers enforce rules in their interactions with customers, patients, and everyday people. AI and other emerging digital technologies increasingly mediate these interactions, but sociologists have often overlooked how technology affects relationships between enforcer and enforcee. We argue that centering Erving Goffman’s ideas of “cooling the mark out”—ameliorating tense interactions after a loss of face, status, or self-image—can illuminate how new, data-driven technologies shift roles and relationships in frontline work. We illustrate these processes by drawing on three case studies: self-checkout in retail cashiering, electronic driving logs in commercial vehicle inspections, and prescription drug monitoring programs in pharmacy. We conclude with recommendations for how sociologists of AI can draw on Goffman to theorize about changes in frontline work occasioned by new AI-driven technologies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".