Indigenous Peoples‐related environmental research within the basin of the Laurentian Great Lakes: A systematic map protocol
Bibliographic record
Abstract
Abstract The North American Great Lakes Basin is the homeland for many First Nations, Métis and Native American Tribes. The terrestrial and aquatic ecological systems within this multinational region, which is of spiritual, cultural and subsistence significance to a diversity of Indigenous Peoples, are facing several natural and anthropogenic pressures. While there are many current and past research efforts and projects to address those pressures, the nature and range of environment‐related projects involving Indigenous Peoples or organizations remains unknown. This gap in knowledge presents a unique opportunity to identify and map past and current environmental and ecological research within the Great Lakes involving Indigenous Peoples. A systematic search strategy will be applied to identify and capture peer‐reviewed publications that pertain to past and current environmental research within the Great Lakes basin that involve or are connected to Indigenous Peoples, following the procedures outlined in this systematic mapping protocol. Publications that pertain to environmental and ecological research with, for and by Indigenous Peoples within the Great Lakes, as determined by the use of suitable keywords, will be retrieved from four proposed online bibliographic platforms and databases. Searches will only include peer‐reviewed publications in the English language. Final captures of the search results will be screened in two stages to identify potentially relevant papers. This will take place through (1) title and abstract screening and (2) full‐text analysis. Following the completion of the screening process, remaining papers will be coded and analysed through a narrative synthesis approach and descriptive statistics will be conducted. Environmental research captured through this systematic protocol will be geospatially mapped using the ArcGIS mapping software. It is anticipated that the information obtained from the resulting systematic map will be beneficial for identifying gaps in environmental research to support and inform future initiatives for environmental research planning, policy and decision‐making with, for and by Indigenous Communities within the Great Lakes basin.
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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 | 0.086 | 0.136 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.033 | 0.022 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.016 |
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".