Shedding the cloak of neutrality: A guide for reflexive practices to make the sciences more inclusive and just
Bibliographic record
Abstract
Abstract The environmental sciences community cannot meaningfully address the compounding ecological and societal crises of our time without also addressing epistemic oppression—the persistent, systemic exclusion that dismisses or erases certain forms of expertise in knowledge production and scientific practices. Epistemic oppression is justified by the inaccurate assumption that scientific knowledge is neutral, value‐free, and objective. This assumption persists because science practices omit information about who we are and how we come to know the world in our work. It operates through the construction of knowledge hierarchies at three levels: (1) privileging particular worldviews of individual scientists, (2) privileging certain academic disciplines, and (3) privileging Eurocentric knowledge systems. To limit epistemic harms, we need to acknowledge that the sciences are inherently relational (i.e., emerge out of relationships among scientists and what we study) and situated (i.e., dependent on the social context surrounding knowledge production). By recognizing and reflecting on assumptions of neutrality, we can transform the scientific community toward fostering greater inclusion and acceptance of diverse worldviews, theories of knowledge, and methodologies to simultaneously address today's wicked problems and advance true diversity, equity, and belonging. Moving from concepts to practice, we outline several reflexive strategies and offer examples and guiding questions to acknowledge our standpoints in scientific research. By embracing reflexivity in our practices, including making our positionality in our work explicit, the environmental sciences can become more inclusive and effective at addressing the compounding crises of this era.
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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 | 0.119 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.014 | 0.145 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".