First Nations Data Governance, Privacy, and the Importance of the OCAP® principles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it