A set of principles and practical suggestions for equitable fieldwork in biology
Bibliographic record
Abstract
Field biology is an area of research that involves working directly with living organisms in situ through a practice known as "fieldwork." Conducting fieldwork often requires complex logistical planning within multiregional or multinational teams, interacting with local communities at field sites, and collaborative research led by one or a few of the core team members. However, existing power imbalances stemming from geopolitical history, discrimination, and professional position, among other factors, perpetuate inequities when conducting these research endeavors. After reflecting on our own research programs, we propose four general principles to guide equitable, inclusive, ethical, and safe practices in field biology: be collaborative, be respectful, be legal, and be safe. Although many biologists already structure their field programs around these principles or similar values, executing equitable research practices can prove challenging and requires careful consideration, especially by those in positions with relatively greater privilege. Based on experiences and input from a diverse group of global collaborators, we provide suggestions for action-oriented approaches to make field biology more equitable, with particular attention to how those with greater privilege can contribute. While we acknowledge that not all suggestions will be applicable to every institution or program, we hope that they will generate discussions and provide a baseline for training in proactive, equitable fieldwork practices.
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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 | 0.160 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.082 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".