Intellectual property and the licensing of Canadian government geospatial data: an examination of GeoConnections' recommendations for best practices and template licences
Bibliographic record
Abstract
In Canada, Crown copyright permits government to assert control over its works. These Crown rights have often been justified on the basis that government must assert intellectual property rights so as to be better able to control the accuracy, integrity and quality of any information that reaches the public through Crown works. In this article, the authors examine GeoConnections' template agreements for the licensing of government geographic data. They argue that not only is the basis and scope of claims to intellectual property rights uncertain, the objectives of quality control, data integrity and accuracy do not appear to motivate the licence terms. The uncertainty as to the legal basis of the intellectual property claims is significant, as licences of this kind may give support to otherwise weak downstream claims by third parties to copyright in data products generated through the use of geographic data provided by the Crown.
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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 | 0.077 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.037 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| 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".