Proceedings of the OHBM Hackathon 2023
Bibliographic record
Abstract
The OHBM Brainhack 2023 integrated educational innovation, creative expression, and scientific exploration to cultivate a dynamic and inclusive environment for learning and collaboration. The event featured two main components: the Train-Track, which provided structured and flexible hands-on training in a study group setting, and the Hack-Track, where participants engaged in open-source project development. To enhance engagement, the Buddy System facilitated peer support, ensuring newcomers felt welcomed within the Brainhack community. A novel addition to this year’s event was the Rhyming Battle, which encouraged participants to creatively express their experiences through scientific humor and artistic wordplay. Additionally, the introduction of the Mini-Grant Initiative aimed to recognize projects that prioritized open science, diversity, and interdisciplinary collaboration. While this initiative received mixed feedback due to its competitive nature, it provided valuable insights for future iterations. Here we reflect on the successes and lessons learned from Brainhack 2023, underscoring the collective impact of these initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".