Bibliographic record
Abstract
A new kind of data revolution is unfolding around the world, one that is unlikely to be on the radar of tech giants and the power brokers of Silicon Valley. Indigenous Data Sovereignty (IDSov) is a rallying cry for Indigenous communities seeking to regain control over their information while pushing back against data colonialism and its myriad harms. Led by Indigenous academics, innovators, and knowledge-holders, IDSov networks now exist in the United States , Canada , Aotearoa (New Zealand), Australia , the Pacific , and Scandinavia , along with an international umbrella group, the Global Indigenous Data Alliance (GIDA) . Together, these networks advocate for the rights of Indigenous Peoples over data that derive from them and that pertain to Nation membership, knowledge systems, customs, or territories. This lens on data sovereignty not only exceeds narrow notions of sovereignty as data localization and jurisdictional rights but also upends the assumption that the nation state is the legitimate locus of power. IDSov has thus become an important catalyst for broader conversations about what Indigenous sovereignty means in a digital world and how some measure of self-determination can be achieved under the weight of Big Tech dominance.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".