First Nations Data Governance, Privacy, and the Importance of the OCAP® principles
Bibliographic record
Abstract
IntroductionGovernance of First Nations data and information requires important considerations that go beyond those typically used in research. Researchers are generally not trained in how to work appropriately within the realm of First Nations data. Further, while Canadian legislation protects individual privacy, First Nations’ community privacy is not protected. Objectives and ApproachThe OCA® principles were created to fill these identified gaps. OCAP® is an acronym that outlines principles regarding the collection, use, and disclosure of data or information regarding First Nations. The letters in OCAP® describe four key principles: Ownership, Control, Access and Possession. ResultsFirst Nations OCAP® principles are beginning to make a paradigm shift in research. This shift in applying OCAP® is changing the standard for First Nations’ data and information. These principles give First Nations sovereignty over their data and information when applied appropriately. The principles go beyond the protection of individual privacy to include the additional consideration of community privacy, a vital issue when working with First Nations’ data. Conclusion/ImplicationsOCAP®, when effectively applied, is a bridging tool for both First Nation communities and researchers to engage in relevant, reciprocal, and practical research projects to tell a story, provide insight, and effect policy change.
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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 | 0.290 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.031 | 0.019 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".