Characterizing New Channels of Communication: A Case Study of Municipal 311 Requests in Edmonton, Canada
Bibliographic record
Abstract
City governments around the world are developing and expanding how they connect to citizens. Technologies play an important role in making this connection, and one frequent way that cities connect with citizens is through 311-style request systems. 311 is a non-emergency municipal notification system that uses telephone, email, web forms, and increasingly, mobile applications to allow citizens to notify government of infrastructure issues and make requests for municipal services. In many ways, this process of citizen contribution mirrors the provision of volunteered geographic information, that is spatially-referenced user generated content. This research presents a case study of the city of Edmonton, Canada, an early adopter of multi-channel 311 service request systems, including telephone, email, web form, and mobile app 311 request channels. Three methods of analysis are used to characterize and compare these different channels over three years of request data; a comparison of relative request share for each channel, a spatial hot spot analysis, and regression models to compare channel usage with sociodemographic variables. The results of this study indicate a shift in channel usage from traditional to Internet-enabled, that this shift is mirrored in the hotspots of request activity, and that specific digital inequalities exist that reinforce this distinction between traditional and Internet-enabled reporting channels.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".