Digital Data Management as Indigenous Resurgence in Kahnawà:ke
Bibliographic record
Abstract
Indigenous peoples are addressing the ongoing impacts of settler colonialism through a variety of expressions of community resurgence. Among these initiatives are those leveraging digital technologies. In the emergent network society, digital infrastructures, and information and communication technologies are powerful tools that can support self-government. In this context, we document the development of digital data management in the Mohawk community of Kahnawà:ke. Data is the digital information generated by a community, encompassing areas like research, education, finance, health, membership, housing, lands, and resources. As self-determining political entities, each First Nation determines how this data is interpreted and used, supported by tools like data management platforms and information-sharing protocols. In this article, we show how local practices regarding the collection, use, and sharing of digital data in Kahnawà:ke provides a clear example of Indigenous resurgence.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".