Measurement and analysis of citizens requests to government for non-emergency services
Bibliographic record
Abstract
311 has been the largest and one of the most important customer service bridge between citizens and government, being actively operated in multiple cities in North America. It enables accessibility for citizens to non-emergency city services and increases efficiency and effectiveness of relevant government authorities in responding to public inquiries. Reporting service requests are easy and fast since they are available online, or can be downloaded on mobile devices as well as via telephone. The high accessible reporting process generated a huge amount of data during the past few years. In this paper, we present a comprehensive analysis of the 311 services for New York state, i.e., NYC 311 services. The NYC 311 dataset is a publicly accessible dataset that contains approximately 15 million records from 2010 to present. We have analyzed multiple features including the number of created requests in terms of responding agency, complaint type, city and time of the day. We also analyzed the completion period and delay period for requests in terms of agency, complaint type, city, years, months and weekdays.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".