Weaving Indigenous knowledge systems and Western sciences in terrestrial research, monitoring and management in Canada: A protocol for a systematic map
Bibliographic record
Abstract
Abstract Human activities and development have contributed to declines in biodiversity across the globe. Understanding and addressing biodiversity loss will require the mobilization of diverse knowledge systems. While calls for interdisciplinary practices in environmental research date back decades, there has been a more recent push for weaving multiple knowledge systems in environmental research and management, specifically Indigenous knowledge systems (IKS) and Western sciences. The use of multiple knowledge systems in environmental research can improve understanding of socio‐ecological connections, build trust in research findings and help implement evidence‐based action towards biodiversity conservation. Mobilizing multiple types of knowledge in environmental research and management can be beneficial; however, challenges remain. There is a need to understand how and where studies have woven IKS and Western sciences together in order to learn about frameworks and processes used, and identify best practices. Here, we present a protocol for a systematic map that will examine the extent, range and nature of the published literature that weaves IKS and Western sciences in terrestrial ecosystems research, monitoring and management in Canada. The systematic map will aim to capture all available and relevant studies found in the published academic and grey literature. The search will use standardized search terms across four publication databases, four specialized websites and one web‐based search engine. Bibliographies of relevant review articles captured by our search strategy will be cross‐checked to identify additional studies. Calls for evidence among professional networks will also complement the search strategy. All searches will be conducted in English. Search results will be reviewed in two stages: (1) title and abstract and (2) full text. All screening decisions at the full‐text stage will be included into the map database. The systematic map will use a narrative synthesis approach employing descriptive tables, statistics and figures (including a map with geospatially referenced studies) to summarize findings. Results from this mapping exercise can serve to support environmental research and management efforts working across IKS and Western sciences by highlighting best practices, as well as evidence gaps.
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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 | 0.189 | 0.255 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".