Neuroscience without borders: Preserving the history of neuroscience
Bibliographic record
Abstract
Over the last 50 years, neuroscience has enjoyed a spectacular development, with many discoveries greatly expanding our knowledge of brain function. Despite this progress, there has been a disregard for preserving the history of these discoveries. In many European countries, historic objects, instruments, and archives are neglected, while libraries and museums specifically focusing on neuroscience have been closed or drastically cut back. To reverse this trend, the Federation of European Neuroscience Societies (FENS) has organized a number of projects, including (a) the History of Neuroscience online projects, (b) the European Brain Museum Project (EBM), (c) the History online library, (d) the FENS meeting History Corner, (e) history lectures in historic venues, and (f) a series of history seminars in various European venues. These projects aim to stimulate research in, and increase awareness of, the history of European neuroscience. Our seminars have attracted large audiences of students, researchers, and the general public, who have supported our initiatives for the preservation of the history of neuroscience for future generations and for the promotion of interest in the history of neuroscience. It is therefore urgent to develop new methods for preserving our history, not only in Europe but also in the rest of the world, and to increase greatly teaching and research in this important aspect of our scientific and cultural legacy.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".