Bibliographic record
Abstract
Abstract The geographic patterns of dentists have seldom been studied beyond very broad assessment. This article demonstrates, with location quotient and hot‐spot analyses, the unevenness of general dentist and dental specialist resources throughout the Canadian province of Ontario and the presence of “dental clusters” within the metropolitan areas of Toronto, Ottawa, Hamilton, London, and Windsor. While many municipalities in the province have no or relatively few dental offices, municipalities that are comparatively rich in both general and specialized dentists are rare and tend to be part of larger urban environments (typically census metropolitan areas), characterized by higher growth rates, higher median incomes, and lower median ages. Dentist office hot‐spots within metropolitan areas occur in or near the downtown and in more peripheral/suburban locales. “High sales” dentist offices in suburban hot‐spots appear to benefit from prosperous locations and supply deficits nearby, whereas inner‐city “dentist districts” may form in part because of localization economies. Understanding where relative dentist under‐supply occurs within a jurisdiction and why other locations are dentist‐rich is important from a policy perspective so that geographic inequities, and associated spatial accessibility issues, can be more comprehensively appraised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".