Open government data and environmental science: a federal Canadian perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments worldwide are releasing data into the public domain via open government data initiatives. Many such data sets are directly relevant to environmental science and complement data collected by academic researchers to address complex and challenging environmental problems. The Government of Canada is a leader in open data among Organisation for Economic Co-operation and Development countries, generating and releasing troves of valuable research data. However, achieving comprehensive and FAIR (findable, accessible, interoperable, reusable) open government data is not without its challenges. For example, identifying and understanding Canada’s international commitments, policies, and guidelines on open data can be daunting. Similarly, open data sets within the Government of Canada are spread across a diversity of repositories and portals, which may hinder their discoverability. We describe Canada’s federal initiatives promoting open government data, and outline where data sets of relevance to environmental science can be found. We summarize research data management challenges identified by the Government of Canada, plans to modernize the approach to open data for environmental science and best practices for data discoverability, access, and reuse.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.007 | 0.030 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it