Indigenous Data Sovereignty and Policy
Bibliographic record
Abstract
This book examines how Indigenous Peoples around the world are demanding greater data sovereignty, and challenging the ways in which governments have historically used Indigenous data to develop policies and programs. In the digital age, governments are increasingly dependent on data and data analytics to inform their policies and decision-making. However, Indigenous Peoples have often been the unwilling targets of policy interventions and have had little say over the collection, use and application of data about them, their lands and cultures. At the heart of Indigenous Peoples’ demands for change are the enduring aspirations of self-determination over their institutions, resources, knowledge and information systems. With contributors from Australia, Aotearoa New Zealand, North and South America and Europe, this book offers a rich account of the potential for Indigenous data sovereignty to support human flourishing and to protect against the ever-growing threats of data-related risks and harms. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9780429273957, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".