Local communities and natural resources: ethnobiology in practice
Bibliographic record
Abstract
Introduction This chapter focuses on methods specific to the field of ethnobiology. Literally, ethnobiology is the study of the logic of life (the logos of bios ) among a group of people ( ethnos ) (Glover 2005: 24). Ethnobiologists examine the knowledge systems that social and cultural communities have developed to explain the natural world. Ethnobiology is also a multidisciplinary field, which means that ethnobiologists use a variety of interdisciplinary methodologies and theories. Ethnobiologists often occupy multiple roles as social and natural scientists. Finally, ethnobiology is at the same time a positivist and interpretative field of inquiry. Scholars conducting ethnobiological research employ a wide range of quantitative and qualitative techniques. The history of the field has been split into several phases to account for the different schools of thought that have dominated ethnobiology over the years (Ellen 2006; Nazarea 2006; Hunn 2007). The initial phase was a period where researchers were conducting “salvage” ethnobiology in an attempt to document local biological knowledge of economically and medically useful plant species (Ellen 2006: S2; Hunn 2007). Phase two began in the 1950s but peaked in the 1960s and 1970s with the rise of cognitive anthropology. Inspired by linguistic studies of the Prague School (circa 1920s), cognitive anthropologists sought to document and classify folk biological knowledge in order to understand how different cultural groups conceptualize their environment (Nazarea 2006). In this strand, ethnoscientists were primarily concerned with methodologies that could be utilized to elicit local categories while at the same time searching for non-local, cross-cultural grids with which to compare cultural groups. From a theoretical perspective, much of the work generated from this phase focused on demonstrating why it is “notable that nonliterates know so much about nature” (Berlin 1992: 5). Conklin’s agroecological study (1954) of the Hanunóo (Philippines), and Hunn’s (1977) and Berlin, Breedlove, and Raven’s (1974) on Tzeltal Maya classification are all exemplary of this period.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".