DRAFT Guidebook for Community-Driven Data Management in the Arctic - Summary and Table of Contents (Alaska and northwestern Canada, 2024-2025)
Bibliographic record
Abstract
This project has supported the development of a “Guidebook for Community-Driven Data Management in the Arctic”, designed to strengthen planning, implementation, and long-term stewardship of data generated through community-based research and monitoring programs. Community-driven research plays a critical role across Arctic regions by documenting environmental change, supporting food security and hazard mitigation, safeguarding Indigenous knowledge and place-based observations, and advancing local priorities. As attention to Indigenous sovereignty and leadership in research has grown, so too has the need for practical guidance on data management that centers community control, governance, and use of data. The guidebook addresses a recognized gap by framing data management as more than technical storage, instead emphasizing questions of ownership, access, sensitivity, sharing conditions, technology choices, and long-term sustainability. It is structured to (1) introduce foundational concepts and terminology in accessible language; (2) provide tools and templates to support collaborative data management planning and agreements; (3) offer guidance on sustaining data systems and meaningful data use over time; and (4) present case studies from Alaska and Northwest Canada illustrating real-world challenges and successes. This dataset is a draft summary of the guidebook along with a table of contents and is not the full/completed guidebook. The guidebook will be published and this entry will be updated as soon as it is completed.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.170 |
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".