Putting humans back in the loop of machine learning in Canadian smart cities
Bibliographic record
Abstract
Abstract Even as researchers recognize smart cities as sociotechnological assemblages, the attractiveness of artificial intelligence and machine learning (AI/ML) continues to drive cities towards automating urban process and analysis. Rather than arguing whether researchers should automate their analysis, we are interested in identifying where non‐automated, manual judgement calls are made and the analysis is more subjective than presented. We examine topic modelling, an ML method, in an analysis of applications to a pan‐Canadian smart cities grant competition. We document 11 steps in topic modelling from data collection to interpretation. At each step, including the choice of the topic modelling method, some degree of human intervention is required. We draw on human‐centred ML research to argue for a greater recognition of the role of humans‐as‐researchers to preclude further uncritical adoption of AI/ML to research smart cities.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".