The Global Neuroanatomy Network: A new repository of open educational resources
Bibliographic record
Abstract
Many parts of the world, especially low- and middle-income countries, lack access to supplemental, time-efficient, and engaging teaching resources. Additionally, many anatomy educators may feel ill-equipped to teach in neuro-related fields. To address these issues, the Global Neuroanatomy Network (GNN) is a new repository of open educational resources (ROER) developed for neuroanatomy educators worldwide. The GNN expands on existing ROERs within health professions and anatomical sciences education while filling the neuroanatomy gap through peer-reviewed, multilingual teaching resources and clinical cases. Funded by the American Association for Anatomy, the GNN is freely available to neuroanatomy educators at all academic institutions. GNN members can submit their teaching resources or clinical cases for peer review and view or download content that global colleagues have submitted. The GNN aims to enhance neuroanatomy education by creating and supporting the expansion of a novel repository and further growing a community of neuroanatomy educators.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".