To Hive or to Hold? Producing Professional Authority through Scut Work
Bibliographic record
Abstract
This paper examines how professionals working in bureaucratic organizations, despite having formal authority, struggle to enact authority over the clients they advise, transforming their right to command into deference to commands. Drawing on a comparative ethnographic study of two professional groups overseeing compliance in university laboratories, I identify how choices about their task jurisdiction influence each profession’s ability to enact authority over and gain voluntary compliance from the same group of clients. One group constructs its work domain to include not only high-skilled tasks that emphasize members’ expertise but also scut work—menial work with contaminated materials—through which they gain regular entry into clients’ workspaces, developing knowledge about and relationships with clients. Using these resources to accommodate, discipline, and understand clients, they produce relational authority—the capacity to elicit voluntary compliance with commands. The other group outsources everyday scut work and interacts with lab researchers mostly during annual inspections and training, which leads to complaints by researchers to management and eventual loss of jurisdiction. The findings show the importance of producing relational authority in contemporary professional–client interactions in bureaucratic settings and challenge the relevance of expertise and professional identity in generating relational authority. I show how holding on to, not hiving off, scut work allows professionals to enact authority over clients.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".