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. There is a need for an intersectionality framework that is inclusive of both the local and global diversity of researchers. Here, we present an intersectionality framework called KLOB which structures barriers to academic success into four components: knowledge exchange (K), language (L), obligations (O), and biases (B), and thus helps to think about the cumulative effect of multiple barriers that individuals from different backgrounds encounter to succeed in academic activities such as scientific publishing, which is the primary currency of academic success in our current system. This framework highlights both local and global disparities in socioeconomic, linguistic, and discriminatory factors that determine the opportunity of individual researchers to succeed in academia. We emphasise that individual researchers have no control over most barriers they face because of where and how they were born. Implementing solutions to address barriers associated with KLOB requires a multiscale vision and initiatives that tackle local and global inequities.
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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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.077 |
| Scholarly communication | 0.031 | 0.035 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".