The Oil Drop Experiment: How Did Millikan Decide What Was an Appropriate Drop?
Bibliographic record
Abstract
The oil drop experiment is considered an important contribution to the understanding of modern physics and chemistry. The objective of this investigation is to study and contrast the views and understanding with respect to the experiment of physicists or philosophers of science with those of authors of physics or chemistry textbooks and laboratory manuals. Results obtained show that physicists and philosophers of science do understand that the experiment is difficult to perform even today, primarily because of the difficulty associated with the selection of the appropriate drops and that consensus was achieved in the scientific community after a bitter dispute between R.A. Millikan and F. Ehrenhaft. In contrast, authors of physics and chemistry textbooks and laboratory manuals ignore the controversy (especially with respect to the selection of the drops) and present an inductivist interpretation in which empirical data were crucial in the quantization of the charge of the electron. By highlighting the difference between the methodologies of Millikan and Ehrenhaft, textbooks can facilitate students' conceptual understanding of the experiment and thus stimulate interest. It is concluded that although experimental data are important, epistemologically their interpretation through conflicts and controversies is even more important.
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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 | 0.023 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".