Co-developing Openness: Indigenous Knowledge and Data Governance and Open Science in Canada
Bibliographic record
Abstract
Open science (OS) is a movement towards making the scientific process, and its outputs, more, transparent, accessible, inclusive, and credible, and OS has become a Government of Canada policy including the Canadian Commission for UNESCO.But how does open science impact Indigenous knowledge and data governance?Indigenous knowledge (IK) and Indigenous data and information challenge typical data and information norms in how they are collected, used, disseminated, and governed.As the Government of Canada is working to advance reconciliation and renew the relationship with Indigenous Peoples, there is an increased focus on institutionally recognizing and promoting the Indigenous right to knowledge and data governance.Although open science will likely increase the accessibility of scientific outputs, including research and knowledge published according to open standards, it is reasonable to assume that some of this research will include local and traditional knowledge and/or Indigenous data and therefore an OS framework should be developed in a way that prioritizes Indigenous knowledge and data governance.In that light, in this master's thesis I aim to answer the following research question: How do the ideals of data, information, and knowledge sovereignty and governance, compare with those of open science, and is an Indigenous open science possible?I do so by conducting a literature review, a semi-structured interview with a First Nation Elder, and a comparative content analysis, and by analyzing the goals, objectives, standards, processes, and knowledge governance practices of both open science and Indigenous knowledge praxis.Here I consider the field of western science to have been shaped by colonial forms of empirical knowledge production and I argue that there are different world views, including Indigenous ways of knowing, that ought to be considered in the development of an OS framework to ensure a truly inclusive and mutually beneficial OS movement in Canada.References ...
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.029 | 0.033 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".