Exploring the Boundaries of Web Map Services: The Example of the Online Injury Atlas for Ontario
Bibliographic record
Abstract
Abstract Injury from causes such as falls, traffic accidents, or violence is a major public health issue globally. Injury prevention research aims to identify vulnerable populations and places by analyzing the spatial patterns of demographic and socio‐economic risk factors associated with elevated injury rates. The stakeholders in injury prevention and control are often distributed across government and public health institutions, non‐profits, and even the private sector (e.g. insurance firms). While this situation calls for distributed, online research tools, their implementation may conflict with health data confidentiality and license limitations for socio‐economic data. In this article, we present the Online Injury Atlas for Ontario, which was designed with the explicit goal of making use of, and contributing to, the Canadian Geospatial Data Infrastructure. We propose a service‐based architecture that integrates publicly accessible map services with protected data layers. Thereby, we demonstrate the benefits of using spatial data infrastructures alongside private data at different levels of protection. In addition, we discuss the extensive data processing needs and specific cartographic design requirements of a Web atlas in the health and social sciences domain.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".