Creating Diverse and Inclusive Scientific Practices for Research Datasets and Dissemination
Bibliographic record
Abstract
Diversity, equity, and inclusivity (DEI) are important for scientific innovation and progress. This widespread recognition has resulted in numerous initiatives for enhancing DEI in recent years. Although progress has been made to address gender and racial disparities, there remains to be biases that limit the opportunities for historically underrepresented individuals to succeed in academia. As members of the Organization for Human Brain Mapping (OHBM) Diversity and Inclusivity committee (DIC), we identified the most challenging and imminent obstacles towards improving DEI practices in the broader neuroimaging field. These obstacles include the lack of diversity in and accessibility to publicly available datasets, barriers in research dissemination, and/or barriers related to publishing. In order to increase diversity and promote equity and inclusivity in our scientific endeavors, we suggest potential solutions that are practical and actionable to overcome these barriers. We emphasize the importance of the enduring and unwavering commitment required to advance DEI initiatives consistently. By doing so, the OHBM and perhaps other neuroscience communities will strive towards a future that is not only marked by scientific excellence but also characterized by diversity, inclusivity and equitable opportunities for all including historically underrepresented individuals internationally.
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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 | 0.743 | 0.827 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.047 | 0.041 |
| Open science | 0.011 | 0.059 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".