Towards an Indigenist data management program: reflections on experiences developing an atlas of sea ice knowledge and use
Bibliographic record
Abstract
The International Polar Year (2007–2008) (IPY) established an IPY data policy that guides a formal data management (DM) process. The DM system envisioned includes data based on Indigenous knowledge systems linked to data collected in the Western scientific tradition. Based on experiences developing an online Atlas of Inuit Sea Ice Knowledge and Use (Siku Atlas) we argue that an ‘Indigenist’ DM program must be developed if the envisioned IPY DM system is to be realized. Existing ideas proposing an Indigenist research paradigm are discussed in the context of DM. To move towards the development and implementation of an Indigenist DM program, we review four key relationships for consideration when documenting Indigenous knowledge and managing the resulting data: Indigenous and Western scientific knowledge systems; communities and researchers; Indigenous knowledge and power; and Indigenous knowledge and documentation methods. To ground the discussion, we link Indigenist DM processes to the Siku Atlas development process and results. Last, lessons learned are presented along with an outline of directions for a research program in support of an Indigenist DM program .
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 | 0.055 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".