Citizen Contacting of Municipal Officials: Choosing Between Appointed Administrators and Elected Leaders
Bibliographic record
Abstract
Although citizen–initiated contacting of municipal bureaucrats has been the subject of extensive research over the past quarter century, there has been relatively little research on the contacting of municipal elected officials or on why citizens might contact elected officials instead of appointed administrators. This research explores that question by using survey data on citizen–initiated contacts with various elected officials and appointed administrators in Atlanta, Georgia. The findings suggest a several–part answer: First, citizensin Atlanta anyway—usually prefer to contact city departments directly rather than through their elected officials, presumably because most contacts involve concerns about municipal services that a department must eventually address. Second, citizens contact both departments and elected officials for many of the same reasons; the most prominent reason is perceived problems with services. Third, the contacting of elected officials appears to be influenced by frustration with the bureaucracy (i. e., dissatisfaction with bureaucratic helpfulness when the bureaucracy is contacted) and also by ignorance of the bureaucracy (i.e., not knowing who to contact there). We conclude this article with a discussion of the possible implications of the findings for public administrators.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".