Visualizing climate change: a systematic scoping review of digital climate knowledge centers for Indigenous communities in Canada, the United States, Australia, and New Zealand
Bibliographic record
Abstract
Digital climate knowledge centers, which serve as virtual hubs by providing crucial data and adaptation information, are essential for addressing the specific impacts of climate change on Indigenous communities. Indigenous Peoples face unique vulnerabilities due to climate change threats to food security, water resources, and cultural continuity. The objective of this systematic scoping review is to contextualize these challenges by synthesizing information from the published literature on the methods/approaches, findings, and scope of research that addresses the co-building of digital climate knowledge centers with Indigenous Peoples in high-income countries. A structured literature search in four major databases yielded 40 relevant peer-reviewed articles focusing on Indigenous Peoples in Canada, United States, Australia, and New Zealand. Several key themes emerged, including the importance of drawing on both Indigenous and Western knowledge systems to create these climate knowledge centers, the role of community-based participatory research in aligning with place-based community interests, and the need for frameworks that support Indigenous self-determination and ensure the protection of intellectual property rights. In this study, we also emphasize the importance of integrating Indigenous sovereignty principles to dismantle oppressive systems and promote initiatives of collaborative and participatory approaches to developing digital climate knowledge centers tailored for Indigenous communities.
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 | 0.031 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".