Bibliographic record
Abstract
Our goal for this special issue was to expand and deepen the understanding and application of the concept of intersectionality, recognizing its untapped potential. We invited open contributions from diverse academic and practitioner viewpoints, encouraging submissions from both the Global South and North. We aimed to explore various interpretations of intersectionality’s value across multiple contexts and disciplines. Using Collins and Bilge’s (2016) framework, we conceptualized intersectionality as a theoretical lens, analytical strategy, and form of praxis throughout our editorial process. We embraced these dimensions organically while maintaining sensitivity in our double-anonymous review. By sharing these insights, we reflect on our own (un)learning and its application to our academic practices. This editorial also serves as an introduction to the articles featured in this special issue, advancing the scholarly discourse on intersectionality and promoting ongoing dialogue in future research and practice.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.094 | 0.048 |
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".