Bridging Indigenous and science-based knowledge in coastal and marine research, monitoring, and management in Canada
Bibliographic record
Abstract
Abstract Background Drawing upon multiple types of knowledge (e.g., Indigenous knowledge, local knowledge, science-based knowledge) strengthens the evidence-base for policy advice, decision making, and environmental management. While the benefits of incorporating multiple types of knowledge in environmental research and management are many, doing so has remained a challenge. This systematic map examined the extent, range, and nature of the published literature (i.e., commercially published and grey) that seeks to respectively bridge Indigenous and science-based knowledge in coastal and marine research and management in Canada. Methods This systematic map applied standardized search terms across four databases focused on commercially published literature, carefully selected specialist websites, and two web-based search engines. In addition, reference sections of relevant review articles were cross-checked to identify articles that may not have been found using the search strategy. Search results were screened in two sequential stages; (1) at title and abstract; and (2) at full text following a published protocol. All case studies included were coded using a standard questionnaire. A narrative synthesis approach was used to identify trends in the evidence, knowledge gaps, and knowledge clusters. Results A total of 62 articles that spanned 71 Canadian case studies were included in the systematic map. Studies across the coastal and marine regions of Inuit Nunangat accounted for the majority of the studies. Whether the focus is on management and decision making or research and monitoring, the predominant ecological scale was at the species level, accounting for over two-thirds of the included studies. There were 24 distinct coastal and marine species of central focus across the studies. Nunavut had the greatest taxonomic coverage as studies conducted to date cover 13 different genera. The predominant methodology employed for combining and/or including Indigenous knowledge was case study design, which accounted for over half of the studies. Other methodologies employed for combining and/or including different ways of knowing included: (i) community-based participatory research; (ii) mixed methods; (iii) ethnography; and (iv) simulation modelling. There are a suite of methods utilized for documenting and translating Indigenous knowledge and an equally diverse tool box of methods used in the collection of scientific data. Over half of the case studies involved Indigenous knowledge systems of the Inuit, while another significant proportion involved Indigenous knowledge systems of First Nations, reflecting 21 unique nations. We found that demographics of knowledge holders were generally not reported in the articles reviewed. Conclusions The results of this systematic map provide key insights to inform and improve future research. First, a variety of methodologies and methods are used in these types of studies. Therefore, there is a need to consider in more detail how Indigenous and science-based knowledge systems can be respectively bridged across subjects while also recognizing specific place-based needs of Indigenous communities. Second, the work highlights the need to better report the demographics of knowledge holders. Further inquiry focused on the extent of knowledge co-production and assessing Indigenous participation across different stages of the research process would serve the research community well to improve future research and monitoring in support of, and to strengthen, evidence-based environmental management.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.051 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| 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".