Alexander von Humboldt: Galvanism, Animal Electricity, and Self-Experimentation Part 1: Formative Years,<i>Naturphilosophie,</i>and Galvanism
Bibliographic record
Abstract
During the 1790s, Alexander von Humboldt (1769-1859), who showed an early interest in many facets of natural philosophy and natural history, delved into the controversial subject of galvanism and animal electricity, hoping to shed light on the basic nature of the nerve force. He was motivated by his broad worldview, the experiments of Luigi Galvani, who favored animal electricity in more than a few specialized fishes, and the thinking of Alessandro Volta, who accepted specialized fish electricity but was not willing to generalize to other animals, thinking Galvani's frog experiments flawed by his use of metals. Differing from many German Naturphilosophen, who shunned "violent" experiments, the newest instruments, and detailed measurement, Humboldt conducted thousands of galvanic experiments on animals and animal parts, as well as many on his own body, some of which caused him great pain. He interpreted his results as supporting some but not all of the claims made by both Galvani and Volta. Notably, because of certain negative findings and phenomenological differences, he remained skeptical about the intrinsic animal force being qualitatively identical to true electricity. Hence, he referred to a "galvanic force," not animal electricity, in his letters and publications, a theoretical position he would abandon with Volta's help early in the new century.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".