The 2020 annual report on the federal progress in implementing open science and its benefits
Bibliographic record
Abstract
The Government of Canada is committed to making federal science, scientific data, and scientists more accessible. The 2020 Annual Report on the Federal Progress in Implementing Open Science and its Benefits describes the Government of Canada’s progress towards that commitment. Progress in implementing open science and the benefits open science can provide is measured through a combination of core metrics designed to capture general progress across all Science-based Departments and Agencies (SBDAs), and supplemental metrics to highlight individual department/agency efforts. Results show an increase in the number of federal peer-reviewed publications available in open access. They also show that although the percentage of overall eligible federal datasets released in the open have only slightly increased, more datasets were made available through Open Maps, an application that provides access to federal geospatial datasets. In terms of public engagement, SBDAs participated in a range of activities allowing Canadians to engage with federal scientists and their research. The reach of federal open science goes far beyond academia. This report shows federal science appears regularly in patents, social media and news outlets. This report also illustrates the benefit associated with open science, for the federal government. Federal science publications that are available in open access are more likely to be cited in other scientific contributions and in patents, which confirms than an open science advantage exists for federal science --meaning that federal scientists who publish in open access can expect their work to be more impactful. While progress in implementing open science has been made, more remains to be done for federal science, scientific data, and scientists to be more accessible. The results of this annual report indicate progress, but fall short of the ambitious targets set out in the Roadmap for Open Science for 2022 and subsequent years. Continued commitment and support of open science is necessary to meet these goals.
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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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".