Between local cultures and national styles: Units of analysis in the history of electroencephalography
Bibliographic record
Abstract
The history of the discovery of the human electroencephalogram (EEG) and the ensuing implementation of electroencephalography is characterized by striking national differences. The first publication on the EEG in 1929 by the German psychiatrist Hans Berger was met with skepticism. Substantial work in this area did not start before the public demonstration of the EEG by the British neurophysiologist Edgar Douglas Adrian in 1934. Soon afterwards, many groups specialized in the new method, particularly in the US, whereas interest remained more limited in France and Britain. A comparative analysis of the rise of electroencephalography has certainly to account for such national differences, but the trajectory of the implementation of this technology calls for an investigation of local research cultures in order to identify units of productivity and to understand the dynamics along this trajectory.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".