Do environmental researchers from marginalized groups experience greater interference? Understanding scientists' perceptions
Bibliographic record
Abstract
Researchers in the environmental studies and sciences play a critical role in influencing real-world decision-making and policies. However, interference during research and sharing of results has been documented in Canada and around the world. Further, research has shown that workers from marginalized social identitie(s) experience discrimination in the workplace. Whether interference in research is related to social identity has never been examined. Using a mixed-methods design, we surveyed 741 environmental researchers in Canada to understand the relationship between social identity (gender, disability status, 2SLGBTQI+ status, race, and perception of racial identity) and reported experiences of interference. Results found that researchers with marginalized identities experienced worse outcomes across 11 of the 25 quantitative measures. For example, most marginalized groups experienced significantly greater fear of misrepresentation by media and (or) fear of negative career consequences due to public commentary, and racialized and disabled persons reported greater external interference in their work (e.g., from management and workplace policy). Given these findings, we express concern that the experience of interference in research can (1) threaten the personal well-being of marginalized researchers, (2) limit the representativeness of information disseminated, thererby impacting environmental decision-making and policy, and (3) contribute to inequities in representativeness of marginalized researchers in environmental sciences in Canada.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".