Approaches and methods used to bring together Indigenous and Environmental science Knowledge in environmental research: A systematic map protocol
Bibliographic record
Abstract
Abstract The bringing together of multiple knowledge sources, such as Indigenous knowledge (IK) and Environmental science Knowledge (ESK), is a topic of considerable interest and significance in environmental research. In the areas of resource management for example, the bringing together of IK and ESK datasets has raised considerable interest for its potential to increase understanding and provide insights into complex phenomena such as the effects of climate change and variability on wildlife health and distribution. The potential benefits that exist from merging these knowledge sources have been widely acknowledged. However, navigating the complex processes involved in knowledge linking continues to pose significant challenges. This systematic mapping protocol will guide the collection and analysis of literature to examine the approaches and methods used in published studies that aim to bring together Indigenous and Environmental science Knowledge in environmental research. The particular focus of this examination is placed on identification of the types of approaches and methods used to merge IK and ESK datasets at the stages of data analysis, results, and interpretation/discussion in the research process. Through a scoping exercise, a draft search string was developed based on a predetermined list of keywords. Consultation was held with a senior Indigenous scholar to advise on the keywords used and consideration for IK likely to be represented in the collected literature. The final search string will be applied to online bibliographic databases to collect studies published in peer‐reviewed journals. The final capture of the search will be screened in two stages: (1) at the level of title and abstract and (2) at full‐text. All studies included will be coded using a standardised coding template and a narrative synthesis approach will be used to identify patterns in the evidence, including knowledge gaps and clusters. Practical implication : The resulting systematic map, following the outlined procedures in this protocol and considering guidelines from the Collaboration for Environmental Evidence (CEE) and Reporting standards for Systematic Evidence Syntheses (ROSES), can serve to support and inform future research endeavours engaged in working towards the linking of IK and ESK, with practical implications for communities and policymakers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".