The need for formal reflexivity in conservation science
Bibliographic record
Abstract
Conservation issues are often complicated by sociopolitical controversies that reflect competing philosophies and values regarding natural systems, animals, and people. Effective conservation outcomes require managers to engage myriad influences (social, cultural, political, and economic, as well as ecological). The contribution of conservation scientists who generate the information on which solutions rely is constrained if they are unable to acknowledge how personal values and disciplinary paradigms influence their research and conclusions. Conservation challenges involving controversial species provide an opportunity to reflect on the paradigms and value systems that underpin the discipline and practice of conservation science. Recent analyses highlight the ongoing reliance on normative values in conservation. We frame our discussion around controversies over feral horses (Equus ferus caballus) in the Canadian West and New Zealand and suggest that a lack of transparency and reflexivity regarding normative values continues to prevent conservation practitioners from finding resilient conservation solutions. We suggest that growing scrutiny and backlash to many normative conservation objectives necessitates formal reflexivity methods in conservation biology research, similar to those required of researchers in social science disciplines. Moreover, given that much conservation research and action continues to prioritize Western normative values regarding nature and conservation, we suggest that adopting reflexive methods more broadly is an important step toward more socially just research and practice. Formalizing such methods and requiring reflexivity in research will not only encourage reflection on how personal and disciplinary value systems influence conservation work but could more effectively engage people with diverse perspectives and values in conservation and encourage more novel and resilient conservation outcomes, particularly when dealing with controversial species.
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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 | 0.547 | 0.547 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.012 | 0.198 |
| Scholarly communication | 0.036 | 0.060 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.018 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 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".