An Examination of Emergency Services Research in Public Administration
Bibliographic record
Abstract
The scholarly field of public administration has long embraced emergency management as a distinct area of inquiry, producing a substantial body of both conceptual and empirical research. However, not all emergency incidents are alike. Larger-scale crises and disasters differ in substantial ways from more “routine” or “everyday” services. We examine and report on the latter, focusing on the conceptual and empirical treatment of routine emergency services in 19 US-based public administration journals from 1999 to 2013. This stock-taking article describes the service focus; the focal topic and purpose of the research; the conceptual or empirical orientation, the unit of analysis, sampling logic and sample size, and data collection sources and methods. Findings indicate that most articles focus on the policing and law enforcement as opposed to fire and emergency medical services. Research foci generally include mainstay topics like human resources, organizational behavior, management, and professionalism, though some focus on topics more specific to these services like community-citizen interactions. This body of existing research is largely exploratory in nature, and primarily uses quantitative data. Directions for future research and concluding comments are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.046 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".