An Analysis of Open Science Action Plans by Canadian Federal Science Departments and Agencies
Bibliographic record
Abstract
Following the release of the Roadmap for Open Science in 2020, Canadian federal departments and agencies that produce or fund scientific research were tasked with developing open science action plans. This study investigates the content and planned implementation of eleven publicly available action plans as of October 2024 using cross-sectional mapping and thematic analysis. The results are examined alongside the Roadmap’s recommendations that directly implicate departments and agencies, including consultations with federal scientists, open access to publication, and enabling FAIR data principles. This study provides insights into how open science activities are understood and operationalized in Canada at the federal level and how the government intends to address obstacles impeding access to federal research. A diversity of approaches to implementing open science practices was observed, along with persistent challenges, including limited mandates for oversight, uneven adoption among smaller departments, and a lack of integration between open science goals and existing research assessment systems. Opportunities lie in strengthening institutional coordination, enhancing horizontal accountability mechanisms, and aligning incentives with open science practices.
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 | 0.042 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".