Navigating Status-Authority Asymmetry between Professions: The Case of 911 Emergency Management
Bibliographic record
Abstract
Status-authority asymmetry between professions emerge when a profession characterized by lower status is mandated by the organization to command and get work done from another profession with higher status and lower formal authority. This, in turn, can undermine cross-professional coordination. How do lower-status professionals with higher formal authority navigate status-authority asymmetry to orchestrate cross-professional coordination with higher-status professionals? To answer this question, I conducted a 24-month ethnography of 911 emergency management, and examined coordination encounters between 911 dispatchers and police officers. I identify a set of practices entailing communication media (open or private radio channels) and relational tactics (personalizing to officers, escalating to supervisors, and publicizing to peers) that 911 dispatchers use during the emergency coordination process. Specifically, I find that as compared to personalizing and escalating, publicizing an individual police officer’s non-compliant behavior to his immediate peer group (i.e., the police unit) via the open radio channel enabled the dispatchers to navigate status-authority asymmetry and orchestrate effective cross-professional coordination. Insofar as the lower-status professionals with higher formal authority (i.e., dispatchers) are able to manage the common information space (in this case, the open radio channel) to create and disseminate peer knowledge about the non-compliant behavior of higher-status professionals’ (i.e., the police officers), then that will in turn trigger peer control and self-disciplining, as the non-compliant individuals’ professional status is on the line in front of the immediate peer group. I discuss the implications of these findings for research on cross-professional coordination and status-authority asymmetry.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".