Spatial Data Quality: The Duty to Warn Users of Risks Associated with Using Spatial Data
Bibliographic record
Abstract
This article discusses whether and when a private provider of spatial data may be liable to pay for damages resulting from physical injury that occurs due to reliance on erroneous spatial data. The existing case law supports the view that some courts will approach harm due to errors in spatial datasets that give rise to physical harm using principles applicable to defective products, while others regard these errors as negligent misrepresentation. This article analyzes the duty to warn and spatial data in two parts. First, it provides an overview of the general problem of spatial data quality and its growing importance in light of internet dissemination to the public. Second, it sketches out the basic rules in the three main subdivisions of Canadian product liability law (manufacturing defects, design defects, and failures to warn of risks associated with products) and applies them to the context of broadly disseminated spatial data.
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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 | 0.028 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".