Ethnobiology Phase VI: Decolonizing Institutions, Projects, and Scholarship
Bibliographic record
Abstract
Ethnobiology, like many fields, was shaped by early Western imperial efforts to colonize people and lands around the world and extract natural resources. Those legacies and practices persist today and continue to influence the institutions ethnobiologists are a part of, how they carry out research, and their personal beliefs and actions. Various authors have previously outlined five overlapping “phases” of ethnobiology. Here, we argue that ethnobiology should move toward a sixth phase in which scholars and practitioners must actively challenge colonialism, racism, and oppressive structures embedded within their institutions, projects, and themselves. As an international group of ethnobiologists and scholars from allied fields, we identified key topics and priorities at three levels: at the institutional scale, we argue for repatriation/rematriation of biocultural heritage, accessibility of published work, and realignment of priorities to support community-driven research. At the level of projects, we emphasize the need for mutual dialogue, reciprocity, community research self-sufficiency, and research questions that support sovereignty of Indigenous Peoples and Local Communities over lands and waters. Finally, for individual scholars, we support self-reflection on language use, co-authorship, and implicit bias. We advocate for concrete actions at each of these levels to move the field further toward social justice, antiracism, and decolonization.
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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 | 0.057 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".