Equity, diversity and inclusion is essential for rigorous science and good for health
Bibliographic record
Abstract
At Diabetologia and Metabologia, we affirm our commitments to the highest standards of scientific excellence and to advancing EDI in diabetes and metabolism research and publishing, imperatives that are inseparably intertwined and essential to achieving optimal well-being for people living with or at a risk of diabetes and other metabolic conditions.Diabetologia's 2025 special issue on global opportunities and challenges for the prevention and treatment of diabetes ( h t t p s : / / d i a b e t o l o g i a -j o u r n a l . o r g / c o l l e c t i o n s / g l o b a l /) exemplifies the journals' commitments to promoting EDI in research and clinical practice.In the special issue, Chaturvedi and colleagues [3] interrogate the problematic, yet common, conflation of ancestry, race and ethnicity and unpack the complexities inherent in defining 'populations' in diabetes research.They argue that variations in population-level diabetes risk and outcomes are not only a product of biological drivers, but are also influenced by the complex interplay of socioeconomic, environmental, political and other determinants of health.Gong and colleagues [4] extend this analysis, demonstrating how disparities in diabetes are fuelled by urbanisation and industrialisation, as well as changing lifestyle factors, energy-dense foods, migration patterns and adverse environmental factors, Abbreviations EDI Equity, Diversity and InclusionIn 2025, the EASD marked two important celebrations: the 60th anniversary of Diabetologia and the launch of its sister journal Metabologia.These noteworthy happenings provide opportunities for us to reflect on the past and to look towards charting the future that we-as scientists, physicians and editors-wish to shape.This charge is especially urgent at a time when, in the USA and other global regions, commitments to equity, diversity and inclusion (EDI) are being rapidly eroded, academic freedom is being increasingly constrained and health policies are being redirected in ways that imperil the health, security and lives of many.Given the widespread disparities in diabetes prevention, care and outcomes [1,2], this blatant assault on EDI practices is especially troubling for people living with diabetes, and the clinical and research communities whose work endeavours to support them.This Editorial is being published simultaneously in Diabetologia.
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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 | 0.050 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".