Data-Centric Content Classification of Smart City Residential Services
Bibliographic record
Abstract
The residential services in the context of smart cities accu- mulate massive real-time inquiry data in natural language to describe the services in need. Such inquiry requests have diverse topics in the content and considerable variate length. Besides, the responsible departments that may handle the inquiry involve a large number of organizations, from metropolitan administration to local communities. Hence accumu- lated request data is primary and central to the service of accurately dispatching requests to responsible departments. The challenge is de- vising a data centric approach to fit the data with SOTA models and improve the request classification accuracy. In this paper, we analyze the factors of embedding tokens, data segmentation, model structures, and classification methods. We devise a unified modelling process with mul- tiple dataflows that combine these factors to observe their interactions. The experiment results demonstrate the compound effects and provide insights into how SOTA models respond differently to variations in these factors. The observations allow us to fine-tune the learning task at each stage and achieve a maximum 82.4% F1-Score.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".