Multi-way correspondence analysis approach to examine Nobel Prize Data from 1901 to 2018
Bibliographic record
Abstract
This study examines Nobel Prize data by studying the association between the nationality of the laureate, the discipline in which the Nobel Prize was awarded, and the gender of the recipient by maintaining the multi-way structure of the data. A three-way contingency table is formed by simultaneously cross-classifying the three categorical variables using a three-way correspondence analysis to assess the association between the three variables. The significance of this study lies in preserving the multivariate associations, which the multiple correspondence analysis approach does not allow. The multi-way correspondence analysis (MWCA) maintains all three-way associations as well as the pair-wise structures between the variables in the case of three variables. The present study consists of 785 individuals from eight developed countries that received a Nobel Prize in the period from 1901 to 2018 (inclusive) – the countries being Canada, France, Germany, Italy, Japan, Russia, British Isles and the United States of America, while the disciplines in which the individuals were awarded the prize include chemistry, physics, physiology or medicine, literature, economics and peace. The results from the MWCA suggest that a strong symmetric association exists between the three variables, in addition, there is a statistically significant association between each pair-wise combination of the variables. The application shows that male physics recipients tend to be from Russia, Japan, and France while female recipients were more likely to be from Japan and France. Furthermore, the analysis highlights that the female medicine recipients are predominantly from the United States of America and the British Isles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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; both teacher heads agree on what is shown here.
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".