311

311 Citizen Service Request Classification — Scouting Report

Compiled 2026-08-28. Background research for building a 311 message classifier.

TL;DR — Ready-to-use models with weights?

No drop-in 311 classifier exists. There is no published Hugging Face model, no Kaggle checkpoint, and no GitHub release with weights specifically trained on 311 taxonomies. You need to fine-tune, or use an LLM zero/few-shot.

Option	Weights?	Fit
Any HF model tagged 311 / civic / service request	❌ none	—
CFPB consumer-complaint fine-tunes on HF (e.g. Dragneel/ticket-classification-v1 DistilBERT)	✅	Adjacent domain, usable as warm start
Generic ticket-routing HF models (trained on Tobi-Bueck/customer-support-tickets, IT-helpdesk)	✅	Even further from municipal, similar short-text routing shape
General base LMs — distilbert-base-uncased, roberta-base, deberta-v3-base, e5-large-v2, BGE-large, all-MiniLM-L6-v2	✅	Realistic starting point: fine-tune or embed+kNN
Zero-shot LLMs (Claude, GPT-4o)	✅ (API)	MDPI 2025 transport-complaint benchmark measured Claude ≈ 89.7%, GPT-4o ≈ 89.0%, GPT-3.5 ≈ 66.9% on a very similar civic-complaint task
Commercial “AI 311” CRMs (OpenGov, CivicPlus/SeeClickFix, Accela)	❌ closed	Not usable as weights

Recommendation: two-track baseline — (1) Claude/GPT-4o zero-shot as the ceiling and (2) DistilBERT or DeBERTa-v3 fine-tuned on NYC 311 as the deployable model. For a warmer start than random init, initialize from a CFPB-complaint DistilBERT before fine-tuning on 311.

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1. Public 311 Datasets

1.1 United States

City	Portal / URL	Volume	Time coverage	Notable text/label fields	License
New York City	data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2020-to-Present/erm2-nwe9 plus 2010-2019 historical	~40M rows (as of Dec 2025) across the two datasets; ~32 columns	2010 → present, daily updates	Complaint Type (~450 unique), Descriptor (~800 unique), Resolution Description (free text), Location Type, Agency, Agency Name	NYC Open Data terms; effectively public / reuse permitted with attribution
Chicago	data.cityofchicago.org/Service-Requests/311-Service-Requests/v6vf-nfxy. Companion Request Types dataset (dgc7-2pdf)	Millions of rows; 91 published request types as of 2019, growing	New 311 CRM launched 12/18/2018; some legacy pre-2018 records flagged LEGACY_RECORD	SR_TYPE, SR_SHORT_CODE, OWNER_DEPARTMENT	Open, City of Chicago terms
Boston	data.boston.gov/dataset/311-service-requests (Analyze Boston); per-year resources (e.g. 2024, 2025). CRM Value Codex is a downloadable PDF at the same dataset page.	~250-300k/year (e.g. 273,951 rows in 2021); ~150 case types	2011 → present; backend system change Oct 2025 (some 2025 requests split between old/new tables)	case_title, subject, reason, type, queue, department, neighborhood, source, closure_reason	Open, City of Boston terms
San Francisco	data.sfgov.org/City-Infrastructure/311-Cases/vw6y-z8j6	Several million; 191k views / 68k downloads	2008-07-01 → present, nightly	Category, Request Type, Request Details, Responsible Agency, Media URL, Source (channel)	Open, DataSF
Los Angeles (MyLA311)	Per-year datasets on data.lacity.org — e.g. 2024, 2025, 2015 → 2025	~1M/year	Aug 2015 → present	RequestType, RequestSource, CDNumber, AssignTo, Owner, Address	Open, City of LA
Washington DC	Per-year datasets on Open Data DC — e.g. 2025 and back to 2009	Millions across years	2009 → present, hourly refresh on “last 30 days” view	SERVICECODEDESCRIPTION, SERVICETYPECODEDESCRIPTION, ORGANIZATIONACRONYM, INSPECTIONFLAG	Open
Philadelphia (Philly311)	opendataphilly.org/datasets/311-service-and-information-requests/; Carto-backed API	Millions	2014-12-08 → present	service_name, service_code, agency_responsible, description (short), requested_datetime	Open
Seattle	data.seattle.gov/City-Administration/Customer-Service-Requests/5ngg-rpne — the “Find It, Fix It” system	Hundreds of thousands	2017 → present (some going back to 2013), quarterly refresh	Request Type, Department, Neighborhood	Open
Kansas City, MO	311 KCMO on data.kcmo.org	20M+ requests referenced in Kontokosta 2021	2007 → present	CASE TYPE, WORK GROUP, DEPARTMENT, SOURCE	Open
Syracuse, NY	SeeClickFix-powered on city portal	Smaller (city size)	2011 → present	Request Type, Category	Open

Many mid-size cities publish through SeeClickFix / Open311 endpoints (below).

1.2 International / Canada

• Toronto — open.toronto.ca publishes “311 Service Requests - Customer Initiated” as one ZIP per calendar year (SR{YEAR}.zip); updated monthly. Coverage advertised Jan 2024 → present with ~1.1M records on file at the time of writing; earlier years also available. Fields include Service Request Type, Division, Section, Ward.
• Montreal — 311 request logs published on the Montreal open data portal (donnees.montreal.ca); French-language TYPE_TACHE and NATURE. Multilingual angle is unique.
• Vancouver — 311 data since 2009 published on opendata.vancouver.ca, aggregated by 22 planning areas.
• UK — FixMyStreet — data.mysociety.org/categories/fixmystreet/. Not government-run but the de-facto UK equivalent. Aggregate counts by LSOA/LAD publicly downloadable; coordinate-level reports available for research use. 11-year longitudinal study exists.
• London Datastore — data.london.gov.uk hosts borough-level complaint datasets.

1.3 Aggregators / cross-city

• 311info.com — normalizes ~24 US/Canadian cities into cross-city categories using an LLM-assisted mapping; publishes the mapping table. Handy prior art for taxonomy reconciliation.
• Andrew Friedman “National 311 Data Portal” — jkan static catalog aggregating city 311 datasets.
• Open311 GeoReport v2 — the API standard. service_code is defined per-jurisdiction; there is no cross-city standard taxonomy.
• SeeClickFix Open311 endpoint — seeclickfix.com/open311/v2/docs. Underpins hundreds of small/mid US cities.
• MotherDuck sample — NYC 311 published as a DuckDB-queryable sample.

1.4 On Kaggle & Hugging Face

• Kaggle: san-francisco/sf-311-cases, chicago/chicago-311-service-requests, new-york-city/ny-311-service-requests, city-of-seattle/seattle-csr-public-requests, shubhammore12/nyc-311-customer-service-requests-analysis.
• One prior Kaggle competition on 311: “NYC 311 Service Level Agreements” — predicts time-to-close, not classification.
• Hugging Face: no canonical 311-classification benchmark exists as of this scout. Adjacent ticket-classification sets: Tobi-Bueck/customer-support-tickets (61.8k, EN+DE), Console-AI/IT-helpdesk-synthetic-tickets, phi-ai-info/support_tickets, Dragneel/ticket-classification-v1 (a DistilBERT model). This is a gap you could fill.

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2. Taxonomies / Label Spaces

No standard. Each jurisdiction defines its own service_code/Complaint Type. Some observed shapes:

• NYC — hierarchical, ~2 levels. Complaint Type (~450 unique historically, ~200 active) is the coarse level. Descriptor (~800 unique) is the fine level. There is a many-to-many Agency mapping (NYPD, DOB, DSNY, HPD, DEP, DOT, DPR, TLC, DOF, DOHMH are the big responders). NYC also publishes a helper table NYC 311 Complaint Type / Descriptor / Count enumerating co-occurrences.
• Chicago — flat, ~91 request types in the modern (Dec 2018+) CRM, each mapped to an OWNER_DEPARTMENT. Old system used one dataset per type (~12 types).
• Boston — 2 levels: reason (broad, e.g. “Streets”, “Trash and Recycling”) + type (~150 case types). The CRM Value Codex PDF is the authoritative list.
• DC — 2 levels: SERVICECODE (broad) / SERVICETYPECODEDESCRIPTION (narrow) with ~200 fine codes.
• Philadelphia — flat, ~100+ service_name values.
• LA — flat MyLA311 RequestType with ~15-20 high-level categories (bulky items, dead animal, e-waste, graffiti, homeless encampment, illegal dumping, metal/household, streetlight, pothole, weed abatement, etc.).
• Seattle — flat Request Type aligned to responsible department.

Common conceptual cluster (present in almost every city): Streets/Potholes/Sidewalks, Sanitation/Trash/Recycling, Noise, Graffiti, Illegal Parking, Trees/Parks, Streetlights, Rodents/Pests, Water/Sewer, Housing/Building Code, Homeless/Encampment, Animal Services, Abandoned Vehicle. That common core is roughly the space 311info uses.

Salient patterns:

• Long tail. In NYC a handful of complaint types (Noise, Illegal Parking, HEAT/HOT WATER, Blocked Driveway, Street Condition) dominate; hundreds of types have <0.1% share.
• Overlap / ambiguity. e.g. NYC has both “Noise” and “Noise - Residential” and “Noise - Street/Sidewalk”; multiple descriptors of “Loud Music/Party” exist. Boston distinguishes ~6 flavors of noise.
• Agency vs. type. Agency assignment is deterministic in some cities and probabilistic (routed by dispatcher) in others; more than 50% of NYC 311 volume goes to NYPD, so agency prediction is severely class-imbalanced.

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3. Prior Academic Work

3.1 Directly on 311 text/type classification

• Hashemi et al., “Automatic Type Detection of 311 Service Requests Based on Customer Provided Descriptions” — Applied Artificial Intelligence, Taylor & Francis, 2022 (Vol 36, No 1). doi.org/10.1080/08839514.2022.2073717. Standardizes categories across two cities; trains an RNN on customer descriptions; reports >83% generalization accuracy. Closest thing to a direct baseline.

• Kontokosta, Hong, Korsberg, “Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain” — arXiv 1710.02452, 2017; extended version in Sustainable Cities and Society 64 (2021) 102503, par.nsf.gov/servlets/purl/10314387. Uses gradient boosting on 20M+ NYC 311 rows to predict likelihood of HPD violations, then compares to reported complaints to quantify under-reporting.

• Wang, Kontokosta, “Structure of 311 Service Requests as a Signature of Urban Location” — arXiv 1611.06660; PLOS One 2017. Uses the distribution over complaint types as a fingerprint for neighborhoods.

• Agostini, Pierson, Garg, “A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing” — arXiv 2312.11754; AAAI 2024. Applies to NYC flooding 311 reports. Code: github.com/gsagostini/networks_underreporting.

• Liu, Garg, Pierson, “Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing” — arXiv 2204.08620.

• “Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports” — arXiv 2506.08740; AAAI 2026 (Proceedings vol. 41158). Uses 9.6M NYC 311 reports across 139 types + 1M government inspection ratings across 5 types (2021-2023) to train a multiview multioutput GNN.

• “Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents’ Reports” — arXiv 2603.11546, 2026. NYC 311 as the case study; MTAC framework exploiting cross-task invariances to reconstruct urban events (illegal dumping, blocked driveways, sanitary issues) from noisy resident reports.

• “Scaling the Queue: Reinforcement Learning for Equitable Call Classification Capacity in NYC Municipal Complaint Systems” — arXiv 2605.06482, 2026. NYC Department of Buildings; RL agent triaging incoming complaints into {escalate, batch, defer, inspect now} with equity constraints.

• Xu et al., “Determinants of citizen-generated data in a smart city: Analysis of 311 system user behavior” — Sustainable Cities and Society 2020. ScienceDirect. LDA-style analysis.

• Estimating reporting bias in 311 complaint data — Annals of Applied Statistics 19(2), 2025, DOI 10.1214/24-AOAS2003. Marron Institute (NYU) team.

• Wang, Kontokosta, “Bias in smart city governance: How socio-spatial disparities in 311 complaint behavior impact the fairness of data-driven decisions” — Sustainable Cities and Society 2020. ScienceDirect.

• “Predicting demand for 311 non-emergency municipal services: An adaptive space-time kernel approach” — Cities 2018. ScienceDirect. Inhomogeneous Poisson process for demand forecasting.

• Minkoff, “NYC 311: A Tract-Level Analysis of Citizen-Government Contacting” — Urban Affairs Review, SAGE 2016. journals.sagepub.com/doi/abs/10.1177/1078087415577796. Political-science angle.

• “Back to Basics: City Services and 311 Service Requests” — ResearchGate publication 356889999, 2021. Cross-city comparison framing.

• Data(-)based ambivalence regarding NYC 311 data infrastructure — Cultural Studies 35(4-5), 2021. tandfonline.com/doi/abs/10.1080/09502386.2021.1895256. Qualitative critique of the pipeline.

• Schiff & Schiff, “Does collective citizen input impact government service provision? Evidence from SeeClickFix requests” — Public Administration Review, Wiley 2025. doi.org/10.1111/puar.13747. Panel dataset of ~70k requests across 100 SeeClickFix cities.

• “Infrastructure legibility — a comparative analysis of open311-based citizen feedback systems” — comparative study of SeeClickFix vs. Citizens Connect (Boston) engagement patterns.

• CitySolution: A complaining task distributive mobile application for smart city corporation using deep learning — arXiv 2410.12882, 2024. Image-based (not text), 4-class dataset (Damaged Road, Flood, Trash, Homeless People) of 5,494 images. Bangladesh context. Included as a “civic complaint classification” data point.

3.2 Adjacent civic / grievance NLP

• “A zero-shot LLM framework for multimodal grievance classification, urgency scoring, and abuse detection in civic feedback systems” — Scientific Reports (Nature) 2025, s41598-025-32079-7. Microservice architecture, zero-shot semantic routing, sentiment-derived urgency, abuse detection.

• “Automated Classification of Public Transport Complaints via Text Mining Using LLMs and Embeddings” — Information (MDPI) 16(8):644, 2025, doi.org/10.3390/info16080644. Compares Claude, GPT-4o, GPT-3.5 and instruction-tuned embedding models. Reported accuracies: Claude 89.68%, GPT-4o 89.00%, GPT-3.5 66.92%. Directly relevant few-shot benchmark methodology.

• “Global Embeddings, Local Signals: Zero-Shot Sentiment Analysis of Transport Complaints” — Informatics (MDPI) 12(3):82, 2025. 2,400-complaint multilingual corpus, “one encoder any facet” framework.

• “Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model” — Journal of Environmental Management 2024, ScienceDirect. 102,782 environmental complaints (2016-2022).

• “Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling study” — PLOS One 2024, journals.plos.org/plosone/article?id=10.1371/journal.pone.0296855. SBERT+UMAP+HDBSCAN+c-TF-IDF; 104 auto-discovered topics.

• “Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework” — Applied Sciences 15(12):6673, 2025.

• “Explainable Multilingual Civic Complaint Resolution System” — IJRASET. MuRIL embeddings + structured urgency prediction.

• CivicSense: AI-Based Citizen Complaint Analyzer — Atlantis Press proceedings.

• “Design of Emergency Call Record Support System Applying Natural Language Processing Techniques” — Springer 2019. ASR → NER → TF-IDF/SVM call classification. Adjacent (911, not 311).

• “Deep multitask ensemble classification of emergency medical call incidents combining multimodal data” — medRxiv 2020.

• “The Feasibility of Using Machine Learning to Classify Calls to South African Emergency Dispatch Centres” — PMC 8472370.

• “Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching” — PMC 8178387. BERT+word2vec+text augmentation for hierarchical imbalanced customer complaints.

• “Medical chief complaint classification with hierarchical structure of label descriptions” — Expert Systems with Applications 2024. Hierarchical label description use.

• “Recent Advances in Hierarchical Multi-label Text Classification: A Survey” — arXiv 2307.16265. Methods survey directly applicable to NYC’s Type/Descriptor hierarchy.

3.3 CFPB consumer complaint work (methodologically transferable)

• From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints — arXiv 2606.22664, 2026. AUC-ROC 0.78 on relief prediction.
• CFPB Consumer Complaints Analysis Using Hadoop — arXiv 2310.06076, 2023.
• Predictive Analysis of CFPB Consumer Complaints Using Machine Learning — arXiv 2407.06399, 2024.
• NLP-Based Consumer Complaint Assessment — Applied Sciences 2025, doi.org/10.3390/app16125992. BERT reported accuracy ~0.72-0.79 on product classification.
• Think Before You Classify: The Rise of Reasoning LLMs for Consumer Complaint Detection and Classification — Electronics (MDPI) 14(6):1070, 2025.

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4. Prior Industry / Government / Open-Source Work

• Boston’s ML routing pilot — the city crowdsources short user descriptions on a training-data site so that a plain-language sentence (“My street has a hole in it”) can be routed to the pothole intake form. Search-engine-style model. Coverage: StateScoop, Boston.gov “Help us teach the new 311”. In late 2024/2025 Boston selected an “AI-native” 311 platform to replace its legacy CRM (GovTech).
• OpenGov 311 — commercial AI-native 311 CRM (opengov.com/products/government-app-library/311-request-management/). Advertises intake completeness checks, location validation, duplicate detection, auto-categorization.
• CivicPlus SeeClickFix 311 CRM — civicplus.com/seeclickfix-311-crm/.
• Accela / Catalis / NebuLogic — other municipal 311 SaaS with published claims about NLP-based intake routing.
• NYC DOB Data-Driven Enforcement — the equity-centered RL routing framework covered in arXiv 2605.06482 is described by NYC as production-adjacent.
• Harvard Kennedy School Data-Smart City Solutions — datasmart.hks.harvard.edu/news/article/cities-embrace-new-improved-311-services. Overview of city 311 modernization projects.
• GitHub — general purpose exploratory notebooks (dozens exist):
  • doshiharmish/Boston-311-Service-Requests-Analysis-2021
  • mrkeville/SeeClickFix-Analysis-Syracuse-NY
  • AvonleaFisher/Analyzing-NYC-311-Service-Requests
  • github.com/topics/open311 (many small implementations)
  • github.com/SeeClickFix/Open311 — reference implementation of Open311 API.
• Blog posts / capstones (useful as informal baselines):
  • Towards Data Science — “Analyzing and Modelling NYC 311 Service Requests” — text→agency classifier, notes NYPD-heavy majority baseline of ~50%.
  • Bopardikar — “Using ML to Predict LA 311 ETC” — random forest on completion time.
  • NYC Data Science blog — “Detailed Data Analysis: The Rise of NYC 311 Noise Complaints”.
• Kaggle: no public 311-classification benchmark, only exploratory notebooks and an SLA-prediction competition.
• Hugging Face: no published 311 text-classification model or dataset.

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5. Related Adjacent Datasets

Dataset	Why relevant	Size	Access
CFPB Consumer Complaint Database	Product/sub-product/issue text classification with a narrative; canonical benchmark for complaint NLP	~4M+ complaints (~13.8M+ if counting company responses)	consumerfinance.gov/data-research/consumer-complaints/; public API
Customer Support Tickets (Tobi-Bueck/customer-support-tickets)	Text→queue routing, EN+DE	61.8k	HF
IT Helpdesk Synthetic Tickets (Console-AI)	Balanced synthetic training set for ticket routing	~thousands	HF
Kaggle “Customer Support Ticket Dataset” (suraj520)	General ticket classification	~10k	Kaggle
Seattle 911 CAD dispatch (data.seattle.gov) and other municipal CAD dumps	911-side of civic ML; short text + event type	Millions	Open
Reddit r/legaladvice / grievance corpora	Long-tail informal complaint text	Millions	HF, Pushshift
FEMA Disaster reports, US National Highway ARF, USDA Complaint text	Short-narrative government text	Varies	data.gov
20 Newsgroups, DBpedia, AG News	Standard text-classification baselines for pretraining ablations	Small	HF/UCI
HiClass — arxiv.org/pdf/2112.06560	scikit-learn-compatible hierarchical classifier library; useful if you exploit NYC Type→Descriptor hierarchy	—	GitHub
BERTopic, contextualized topic models	For unsupervised discovery when labels drift	—	HF/GitHub

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6. Key Challenges Documented in Prior Work

1. Severe class imbalance. In NYC ~50%+ of requests route to NYPD (mostly noise + illegal parking). A “predict majority agency” baseline is already ~50%. Long tail of low-frequency complaint types under-fit (Hashemi 2022; multiple TDS blog posts; the Chinese customer-complaint text-enhancement paper).

2. Taxonomy drift and inconsistency.
   • Cities re-organize categories periodically (Chicago’s 2018-12-18 CRM rewrite; NYC descriptors added/renamed; Boston’s Oct-2025 backend transition splitting the dataset).
   • Complaint Type / Descriptor co-occurrence is many-to-many; single free-text description often maps to multiple valid labels.
   • No cross-city standard (Open311 leaves service_code to the jurisdiction), so multi-city training requires an explicit label reconciliation step — cf. 311info’s LLM-assisted mapping and Hashemi 2022’s “standardize categories across two cities” step.

3. Free-text ambiguity and short-text sparsity. Boston’s short titles and Philly’s description fields are often <15 tokens; customer descriptions may say “hole in street” (pothole? sinkhole? construction defect?). The MDPI 2025 LLM-vs-embedding paper found Claude ~89.7% but GPT-3.5 ~67% — model choice matters heavily on short civic text.

4. Reporting/geographic bias (fairness). Kontokosta et al. 2017 and 2021, Wang & Kontokosta 2020, Agostini/Pierson/Garg AAAI 2024, Marron/AoAS 2025 — all document that low-English-proficiency, high-unemployment, non-white, and lower-income areas under-report. Training on raw counts therefore learns the bias. This affects downstream classification whenever priors depend on location.

5. Missing-not-at-random outcome labels. Enforcement/inspection outcomes are systematically less recorded in historically underserved areas, so any supervised training on Resolution Description or SLA-met labels inherits that missingness.

6. Multi-jurisdictional generalization. Same phrase (“blocked bike lane”) routes to different owning agencies in NYC, Chicago, LA. Fine-tuning per-city vs. one shared model is an open trade-off.

7. Channel bias. Text quality varies drastically by intake channel — call-center dispatcher transcription vs. mobile-app self-report vs. Twitter/X vs. email. San Francisco survival-analysis work found Open311 vs. Twitter reports have different resolution-time distributions; the same is likely true for text style.

8. Duplicates / near-duplicates. Same event reported many times; deduplication is a documented commercial-CRM feature (OpenGov, Accela).

9. Multilingual and code-switching. Real intake includes Spanish, Chinese, Bengali, French (Montreal), Russian. Most public research has been English-only.

10. Label-vocabulary evolution over time. A 10-year classifier must handle types that didn’t exist earlier (e.g. “e-scooter”, “encampment”), i.e. concept-drift.

11. Aleatoric vs. epistemic uncertainty in tiny categories. ~800 NYC descriptors mean many have <100 training examples; hierarchical multi-label methods (see arXiv 2307.16265 survey; HiClass; Hierarchical MixUp arXiv 2209.13912) become relevant.

12. Data-infrastructure critique. Cao 2021 (Cultural Studies) argues the 311 pipeline itself is not neutral — worth reading if the classifier is exposed to end users.

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7. Suggested Starting Points for Building the Classifier

Given the above scout, the highest-leverage starting configuration is:

• Anchor dataset: NYC 311 (largest, richest text, well-documented hierarchy) → union with Chicago + Boston for cross-city generalization tests. Use 311info’s mapping as a starting-point normalized taxonomy.
• Text field: concatenate Descriptor + Location Type + any Resolution Description for NYC; case_title + subject + reason for Boston; service_name + description for Philly.
• Task variants worth benchmarking:
  1. Coarse Complaint Type classification (~150 classes) — closest to Hashemi 2022 baseline (~83%).
  2. Fine Descriptor classification (~800 classes) — hierarchical, per HiClass / arXiv 2307.16265.
  3. Agency classification (~10-20 classes) — matches Boston’s routing pilot.
  4. Zero/few-shot LLM baseline — reproduce the MDPI 2025 methodology (Claude vs. GPT-4o vs. embedding+kNN).
• Fairness sanity checks: stratify metrics by ZIP/tract using the Kontokosta / Agostini / Marron-Institute methodology.
• Baselines to include: TF-IDF + LinearSVC/LogReg (classic 311 baseline), DistilBERT/BERT fine-tuning, sentence-transformer embeddings + kNN, an LLM few-shot classifier, plus a hierarchical variant.

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Sources (main URLs cited)

Datasets: NYC 2020-present, NYC 2010-2019, Chicago v6vf-nfxy, Chicago Request Types, Boston Analyze Boston, SF vw6y-z8j6, LA 2025, DC 2025, Philly, Seattle 5ngg-rpne, Toronto, FixMyStreet, 311info categories, Open311 GeoReport v2, SeeClickFix Open311 docs.

Papers: Hashemi 2022 (T&F), Kontokosta 2017 arXiv, Wang & Kontokosta 2017 PLOS One / arXiv 1611.06660, Agostini/Pierson/Garg 2024 AAAI arXiv 2312.11754, code repo, Liu et al. 2204.08620, GNN urban incident 2506.08740, MTAC 2603.11546, RL for NYC DOB 2605.06482, Xu Sustainable Cities 2020, Wang & Kontokosta 2020 Sustainable Cities, AoAS 2025 estimating reporting bias, Zero-shot LLM civic Nature Sci Rep 2025, MDPI Info 2025 transport LLM, BERTopic China gov PLOS 2024, Stacking-BERT China env complaints, CitySolution 2410.12882, Hierarchical multi-label survey 2307.16265, HiClass 2112.06560, SeeClickFix panel Schiff PAR 2025, Kontokosta 2021 Sust. Cities PDF.

CFPB: Consumer Complaint Database, arXiv 2606.22664, arXiv 2407.06399, arXiv 2310.06076.

Industry / gov: Boston ML routing StateScoop, Boston “Help us teach the new 311”, OpenGov 311, CivicPlus SeeClickFix, Harvard Data-Smart Cities 311 overview.