The Canadian Brain Research Strategy: A Focus on Early Career Researchers
Bibliographic record
Abstract
THE CANADIAN BRAIN RESEARCH STRATEGY (CBRS)The Canadian Brain Research Strategy (CBRS; canadianbrain.ca), a grassroots initiative that has been building since 2015, reached a turning point in June 2020 with federal financial support from the Canadian Institutes of Health Research to formalize its network infrastructure and strategic activities.The early Canadian team was part of the 14-nation group that co-signed, in 2016, a G-Science Academies Statement on Understanding, Protecting, and Developing Global Brain Resources, emphasizing that " : : : the human brain as civilization's most precious resource and placing investment in brain science as an investment in the future of society : : : " The statement further called for a concerted effort to achieve the goal of overcoming the bottleneck of developing the required technologies " : : : to study the brain at a resolution sufficient to enable understanding of its complex neuronal network in animal models and humans" (National Academies 2016).Following suit, in 2018, Canadian researchers representing the CBRS joined researchers from the USA, Europe, Japan, Korea, China, and Australia to provide leadership to the International Brain Initiative (IBI; internationalbrain initiative.org) with the mission to " : : : catalyze and advance ethical neuroscience through international collaboration, knowledge sharing, united ambitions and the dissemination of discoveries" 1 .The simple subline of the Declaration of Intent to Create the IBI initiative provides its foundational concept: "It takes the world to understand the brain".In this spirit, the CBRS today seeks to anticipate and fuel innovations that expand global boundaries of knowledge and technology, and drive the development of new tools, including artificial intelligence.It relies on ethical frameworks for culturally respectful design and meaningful dissemination of neuroscience research and has, at its core, a priority to build capacity among Canada's future leaders in the neurosciencestoday's Early Career Researchers (ECR)and on whom we focus here.
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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 | 0.053 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".