Brain Mappers of Tomorrow: An international multilingual initiative for neuroscience dissemination
Bibliographic record
Abstract
The initiative "Brain Mappers of Tomorrow" coordinated by the Organization for Human Brain Mapping (OHBM) Diversity and Inclusivity Committee (DIC) aims to make neuroscience accessible to children worldwide, particularly those from historically underrepresented backgrounds. Over the past several years, this successful initiative has grown and evolved, offering live reviews of scientific papers tailored for children in multiple languages. These live review events have seen exponential growth, engaging over 1,000 children in 2023 alone. Through partnerships and innovative strategies, the initiative has successfully reached diverse audiences, fostering curiosity and critical thinking in young minds. Although some challenges remain, including recruiting scientists and participants from underprivileged communities, ongoing efforts strive to overcome these barriers. The success of "Brain Mappers of Tomorrow" demonstrates the potential for similar initiatives across scientific disciplines, emphasizing the importance of diversity and inclusivity in science education and outreach. Such efforts can foster positive impacts at multiple levels, from individuals (children and presenters) to global society. This editorial highlights the benefits and challenges of such initiatives, shares experiences and resources to assist other scientific communities in launching similar endeavors, and discusses future directions.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".