Best practices for measuring community resources across Canada: A comparison of coding classifications
Bibliographic record
Abstract
Abstract Social scientists, geographers, criminologists, and health scientists are often tasked with finding data to best capture the impact of “community context” on individual outcomes, including residential services, physical resources, and social institutions. One outlet for such data in Canada is Digital Map Technologies Inc. (DMTI) Spatial, which offers a national repository of over one million businesses and recreational points of interest. The database is generated through CanMap Streetfiles, which includes geocodes of each point's precise location. These data are available to researchers from their university data library and Esri Canada, but primarily available to private sector and government markets. That said, the goal of the current paper is to encourage researchers to access this rich yet under‐utilized data source. Each service, business, or resource in the DMTI Spatial database is assigned to a respective category using Standard Industrial Classification codes and North American Industrial Classification System codes. It is not clear, however, which is the more reliable coding criteria. We provide an overview of our review of DMTI Spatial data and take‐away suggestions for using this valuable resource for future research on meso‐level residential markers.
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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 | 0.042 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.034 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".