An intersectionality lens is needed to establish a global view of equity, diversity and inclusion
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) have become essential considerations in different academic fields in recent years, attracting an increasing number of voices and perspectives from different groups. While recent contributions have shed light on the barriers faced by some groups, the concept of EDI and implementation of solutions are still in their infancy in ecology and evolution. There is a clear lack of an intersectionality framework that is more inclusive of the global diversity of researchers. As researchers in ecology and evolution from the Global South and Global North with different backgrounds, we recognize the need to present a global view of EDI in order to highlight the role of intersectionality where researchers from Global South are not only impeded by discrimination, but also by other cultural, linguistic, and socioeconomic factors that affect their level of training, ultimately reducing their likelihood of reaching leadership positions. We present a simple model of intersectionality that explains the main drivers of the variation in academic success among researchers, and highlight that most of the variation is determined by factors that individuals have no control over (e.g. place of birth, gender, ethnicity). We recommend measures to increase the representation of the global diversity in the field of ecology and evolution in order to collectively solve global societal and environmental issues.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".