The CARE Principles and the Reuse, Sharing, and Curation of Indigenous Data in Canadian Archaeology
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
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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