Hidden participants and unheard voices? A systematic review of gender, age, and other influences on local and traditional knowledge research in the North
Bibliographic record
Abstract
Local, lay, and traditional ecological knowledge (LTK) is widely discussed in academic studies of climatic and environmental change. Here, we report on a systematic literature review that examines the role of such factors as gender, age, and scholarly networks in shaping LTK research. We focused on research in the circumpolar North, where LTK research has been ongoing for at least four decades. We explored how recruitment approaches and research methods can circumscribe local expertise and found that much of the literature fails to adequately report sampling and participant demographics. There is an apparent bias towards male knowledge-holders, usually hunters and Elders, over women and youth. Studies were largely led by male authors, and male authors outnumbered female authors 2:1. We also identified two potential “invisible colleges” in the literature—communities of practice linked by one or a few authors. We discuss our findings through the lens of “intersectionality”, which captures how power differences at play within communities, whether around age or gender or some other social categorization, contribute to the creation of multiple kinds of knowledge. We conclude with a discussion of how we can improve this area of research by challenging assumptions and collaborating with a wider range of individuals.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".