Bibliographic record
Abstract
Ethical guidance for research involving Indigenous and traditional communities, cultural knowledge, and associated biological resources has evolved significantly over recent decades. Formal guidance for ethnobiological research has been thoughtfully articulated and codified in many helpful ways, including but by no means limited to the Code of Ethics of the International Society of Ethnobiology. We have witnessed a successful and necessary era of “research ethics codification” with ethical awareness raised, fora established for debate and policy development, and new tools evolving to assist us in treating one another as we agree we ought to within the research endeavor. Yet most of us still struggle with ethical dilemmas, conflicts, and differences that arise as part of the inevitable uncertainties and lived realities of our cross-cultural work. Is it time to ask what more (or what else) might we do, to lift the words on a page that describe how we should conduct ourselves, to connecting with the relational intention of those ethical principles and practices in concrete, meaningful ways? How might we discover ethics as relationship and practice while we necessarily aspire to follow adopted ethical codes as prescription? This paper brings together Willie Ermine’s concept of “ethical space” and Darrell Posey’s recognition of the spiritual values of biodiversity with a unique selection of insights from other fields of practice, such as intercultural communication, conflict resolution and martial arts, to invite a new conceptualization of research ethics in ethnobiology as ethical praxis.
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 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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.079 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".