Bridging Indigenous and science-based knowledge in coastal-marine research, monitoring, and management in Canada: a systematic map protocol
Bibliographic record
Abstract
Abstract Background The incorporation of multiple types of knowledge (e.g., science, Indigenous knowledge, traditional ecological knowledge) is an important undertaking, which can strengthen 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, successfully doing so has remained a challenge. In response there has been a number of recent reviews that have sought to better understand the what and how, when it comes to bridging Indigenous and science-based knowledge. Yet there continues to be a need for methods, models, and approaches for integrative work. This systematic map seeks to examine the extent, range, and nature of the published literature (i.e., peer-reviewed and grey) that integrates and/or includes Indigenous and science-based knowledge in coastal-marine research, monitoring, or management in Canada. Results from this study can be used to inform new and ongoing research and monitoring efforts and highlight evidence gaps. Methods The systematic map will aim to capture all available studies relevant to the question found in the peer-reviewed and grey literature. Accordingly, the search will leverage four databases focused on peer reviewed publications, carefully selected specialist websites, and two web-based search engines. Reference sections of relevant review articles will also be cross-checked to identify articles that were not found using 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 will be included in the database. The systematic map will employ a narrative synthesis approach that will include the use of descriptive statistics, tables (including SM database), and figures (including map with the studies geospatially referenced). In addition, an online version of the map and queryable database will be developed similar to other knowledge mobilization tools.
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 | 0.097 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".