Indigenous Metadata Bundle Communique
Bibliographic record
Abstract
In response to this, Collaboratory for Indigenous Data Governance, ENRICH: Equity for Indigenous Research and Innovation Coordinating Hub, and Tikanga in Technology hosted the Indigenous Metadata Symposium on May 9, 2023 at New York University to initiate the development of an Indigenous metadata bundle for use across multiple data systems and repositories. Over 60 participants attended the symposium, including representatives from academia, museums, governmental agencies, and nonprofit organizations across the United States, Canada, Australia, Aotearoa/New Zealand, Europe, and French Polynesia. The goal of the symposium was to initiate the development of an Indigenous Metadata Bundle for use throughout the data ecosystem, including repositories. We are defining the Indigenous Metadata Bundle as a conceptual framework that identifies primary principles that facilitate cross-standard intention and creates the possibility for new fields or elements to be developed that include specificity for Indigenous Peoples Data within a metadata document, bloc, facet or field.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.888 | 0.844 |
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; both teacher heads agree on what is shown here.
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".