The Miiyupimatisiiun Research Data Archives Project: putting OCAP<sup>®</sup> principles into practice
Bibliographic record
Abstract
Purpose The aim of this paper is to document the operationalization of the OCAP® principles in the context of the work of a medical anthropologist and Whapmagoostui First Nation (FN). The authors describe their recent collaboration with Whapmagoostui FN to digitize and transfer the research data archive to the community. Design/methodology/approach Beginning with a description of the data collection process from the late 1980s to early 1990s, this study describes recent efforts to digitize the research data archive and work with Whapmagoostui FN to develop a plan for access and safekeeping. The authors focus on the work required to implement the OCAP® principles locally, including the need to address questions of ownership rights/transfer, information technology systems and community capacity. Findings This study describes the necessary work that is required to operationalize the OCAP® principles on a local level, including obstacles to this work. This study also underscores how the process of OCAP® implementation is distinct for each community and research context. Based on these considerations, the authors calls for increased resources and new legal mechanisms in support of achieving indigenous data sovereignty (IDSov) in FNs, Inuit and Métis communities across Canada. Originality/value To the best of the authors’ knowledge, this study makes an original contribution to the literature on IDSov. This study provides a valuable case study, illustrating how the OCAP® principles can be operationalized in the context of a longstanding partnership between an academic researcher and an indigenous community.
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.
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 | 0.110 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".