CONP Ethics and Data Governance Framework
Bibliographic record
Abstract
The CONP Ethics and Data Governance Framework has been developed by the CONP Ethics and Governance Committee. This document is currently open for comment until August 17, 2019. For more information or to submit comments, please contact: adrian.thorogood@mcgill.ca Canadian Open Neuroscience Platform, Ethics and Governance Committee: Ann Cavoukian, Privacy by Design Centre of Excellence, Ryerson University John Clarkson, Ontario Brain Institute Jennifer Flynn, Division of Community Health and Humanities, Faculty of Medicine, Memorial University Richard Gold, Faculty of Law, McGill University Judy Illes, CM, PHD Division of Neurology, Department of Medicine, University of British Columbia. Bartha Knoppers, Centre of Genomics and Policy, McGill University (Chair) Roland Nadler, Center for Health Law, Policy & Ethics, University of Ottawa Walter Stewart, Walter Stewart and Associates Adrian Thorogood, Centre of Genomics and Policy, McGill University (Manager) The Ethics and Governance Committee would also like to acknowledge the numerous members of the CONP and scientific community who have contributed to this policy. Executive Summary This Framework outlines core ethical elements, general principles, and practical guidance for the neuroscience community in Canada and internationally, as it adopts open science practices and develops supporting information and communication technology (ICT) infrastructure, namely the Canadian Open Neuroscience Platform (CONP). Open science involves the rapid and wide distribution of scientific knowledge, in order to improve scientific collaboration, integrity, and reproducibility; accelerate discovery; and improve human health. If conducted responsibly, open science can foster the human right of everyone to share in scientific advancement and its benefits.1 This Framework focuses on safeguarding the rights and interests of data subjects in open science contexts, which include autonomy, privacy, health, and inclusion. It should be interpreted with reference to the CONP mission.2 1 United Nations, Universal Declaration of Human Rights (1948), art 27. 2 Canadian Open Neuroscience Platform (CONP), “Our Mission” https://conp.ca/
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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 | 0.236 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.036 | 0.010 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 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".