A cloud‐based solution for trustless indigenous data sovereignty: Protecting Māori biodiversity management data in Aotearoa New Zealand
Bibliographic record
Abstract
Abstract Indigenous peoples should be able to govern data about themselves, their territories, resources, and ways of life, collected by themselves or others. However, the progressive use of cloud computing for the geoweb raises data security and privacy concerns. We propose a complete and trustless approach for indigenous geospatial data sovereignty on the cloud by furnishing security functions at the core—the web browser. Geomasking permits sharing an anonymized dataset with less privileged users, while the original is protected and shared with sovereign data owners via public‐key encryption. The encrypted dataset's hash value is notarized on the blockchain for the verification of its authenticity when on the cloud. The application was designed for the protection of Biodiversity Management Areas stewarded by the Māori people in Aotearoa New Zealand. It enables diversified functions of geospatial data protection compared with previous works focusing on the cloud by solving data‐sharing problems without relying on a third party.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".