The global relevance of locally grounded ethnobiology
Bibliographic record
Abstract
While ethnobiology is a discipline that focuses on the local, it has an outstanding, but not yet fully realized potential to address global issues. Part of this unrealized potential is that universalistic approaches often do not fully recognize culturally grounded perspectives and there are multiple challenges with scaling up place-based research. However, scalability is paramount to ensure that the intimate and context-specific diversity of human-environmental relationships and understandings are recognized in global-scale planning and policy development. Here, we identify four pathways to enable the scalability of place-based ethnobiological research from the ground up: local-to-global dialogues, aggregation of published data, multi-sited studies, and geospatial analyses. We also discuss some major challenges and consideration to encourage continuous reflexivity in these endeavours and to ensure that scalability does not contribute to unnecessarily decontextualizing, co-opting, or overwriting the epistemologies of Indigenous Peoples and local communities. As ethnobiology navigates multiple scales of time and space and seeks to increase its breadth, this study shows that the use of deliberately global approaches, when carefully nested within rich field-based and ecological and ethnographically grounded data, can contribute to: (1) upscaling case-specific insights to unveil global patterns and dynamics in the biocultural contexts of Indigenous Peoples and local communities; (2) bringing ethnobiological knowledge into resolutions that can influence global environmental research and policy agendas; and (3) enriching ethnobiology's field-based ethos with a deliberate global analytical focus.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".