The CARE Principles and the Reuse, Sharing, and Curation of Indigenous Data in Canadian Archaeology
Bibliographic record
Abstract
ABSTRACT Reuse and sharing of archaeological data are tied to ethics in data practice, research design, and the rights of Indigenous peoples in decision-making about their heritage. In this article, the authors discuss how the CARE (Collective benefit, Authority to control, Responsibility, and Ethics) principles and Indigenous data governance create intellectual space for archaeological research. We show how archaeologists can use this framework to highlight hidden costs and labor associated with the “data ecosystem,” which are often borne by Indigenous nations and communities. The CARE framework gives voice to Indigenous peoples’ concerns around data sharing, curation, and reuse; ways we can redress these issues; and strategies that facilitate Indigenous nations and communities in deriving collective benefit from research. In archaeology, these efforts include greater work on heritage legislation and policy, repositioning Indigenous peoples as active stewards of their data, and building capacity in digital methods and ethical data practice. Each Indigenous nation and community has its own interests, values, and protocols, and we suggest paths to bring data practice into alignment with the CARE framework.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.038 | 0.076 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".