A primer for the practice of reflexivity in conservation science
Bibliographic record
Abstract
Abstract Rigorous scientific practice relies on the tenet of transparency. However, despite regular transparency in areas such as data availability and methodological practice, the influence of personal and professional values in research design and dissemination is often not disclosed or discussed in conservation science. Conservation scientists are increasingly driven to work in collaboration with communities where their work takes place, which raises important questions about the research process, especially as the field remains largely represented by a Western scientific worldview. The process of reflexivity, and the creation of positionality statements as one form of a reflexive practice, is an important component of transparency, rigor, and best practice in contemporary conservation science. In our own professional practices, however, we have found that guidance on how to produce positionality statements and maintain reflexivity throughout the lifecycle of research is too often lacking. In response, we build on existing literature and our own experience to offer a primer as a starting point to the practice of reflexivity. Rather than being prescriptive, we seek to demonstrate flexible approaches that researchers may consider when communicating reflexive practice to enhance research transparency. We explore the challenges and potential pitfalls in a reflexive practice and offer considerations and advice based on our collective professional experience.
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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 | 0.332 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.116 |
| Scholarly communication | 0.029 | 0.043 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.028 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".