Bridging Indigenous and <scp>Western</scp> sciences: Decision points guiding aquatic research and monitoring in <scp>Inuit Nunangat</scp>
Bibliographic record
Abstract
Abstract When brought together, Indigenous and Western sciences offer holism that can strengthen research and monitoring, yet the practices and processes of bridging these sciences are not well understood. We sought to elucidate bridging through a systematic realist review of coastal and marine research and monitoring studies that use methods for gathering Indigenous scientific knowledges and methods for collecting natural sciences data from across Inuit Nunangat (Inuit homelands in Canada; n = 25 case studies). We identified three decision points that shape projects co‐developed by researchers and Inuit communities: research objectives, method bundles (the totality of methods used in a case study), and method sequencing (the order of application of methods in a case study). Example case studies from the review are included to highlight some of the diversity of research pathways available. We discuss areas for further reflection, including method bundle composition, imbalances in method sequences, path dependency and research fatigue, research context, and most importantly, bridging as a relational rather than technical endeavour. We suggest that bridging sciences can, but need not be, a complex undertaking. This paper provides practical details to facilitate cross‐cultural research partnerships at a time of immense environmental and social change.
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 | 0.109 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".