Weaving Open Dialogue Using Canada’s Open Science Roadmap Framework
Bibliographic record
Abstract
<p>Open science (OS) as a movement has transformative potential in making the process of scientific research transparent and collaborative as well as the outputs freely accessible to all in society. However, these opportunities and challenges are subject to biases and entrenched in power disparities. In addition, the very broad nature of open science also invokes challenges in having meaningful discussions. In 2020, the Government of Canada unveiled a national framework, Roadmap to Open Science, which provided overarching principles and recommendations to allow federal science to be open to all. The University of Toronto (U of T) used this national open science framework to guide an international group of researchers and librarians to discuss open science in practical terms and engage the audience in being part of the dialogue. The five high-level principles of People, Transparency, Inclusiveness, Collaboration, and Sustainability were used as the structure in order to guide discussions into the current state of open science practices on-the-ground in academia. The University of Toronto Library (UTL) partnered with the Centre for Research & Innovation Support (CRIS) to host engaging conversations in a series of five virtual panels, Open Science: Following the Roadmap for Research, held in November 2021. The panelists consisted of librarians, faculty, and researchers from local, national as well as international institutions and organizations. Two core considerations on developing the make-up of the panels were to ensure diversity amongst panelists and have librarians included in every panel. The conversations were thought-provoking and touched-on aspects such as who is included and excluded in the various stages in research, implications of funding and control, infrastructure, power dynamics, and preservation of information. This paper will discuss the open science panel series and common themes which emerged from the conversations.</p> <p> </p>
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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 | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.041 | 0.051 |
| Scholarly communication | 0.044 | 0.021 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".