Seeking a More Ethical Future for Ethnobiology Publishing: A 40-Year Perspective from <i>Journal of ethnobiology</i>
Bibliographic record
Abstract
The academic publishing world is rapidly changing. These changes are driven by and have implications for a range of intertwined ethical and financial considerations. In this essay, we situate Journal of Ethnobiology (JoE) in the discourse of ethical publishing, broadly, and in ethnobiology, specifically. We consider it an ethical imperative of JoE to promote the core values of the field of ethnobiology as a platform for scholarship that is both rigorous and socially just. We discuss here the many ways JoE addresses this imperative, including issues of diversity, accessibility, transparency, and how these efforts contribute to our ongoing relevance. We find that JoE has achieved high ethical standards and continues to raise the bar in our field. However, the growing incongruity between monetary solvency and best practices could threaten JoE's longevity unless we keep adapting to the changing landscape. Looking to the future, we encourage all ethnobiologists to participate in the ongoing process of improving ethics in publishing, including careful consideration of where to publish precious ethnobiological knowledge.
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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 | 0.091 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.038 | 0.060 |
| Scholarly communication | 0.064 | 0.050 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".