Nobel Prize in Economics: retrospectove analysis and prediction of laureates
Bibliographic record
Abstract
The purpose of the article is to analyze retrospective data on the laureates of the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for the years 1969-2022 and to try to predict three parameters of 2023: the number of scientists who will receive the award; the part of the world in which the place of work of the laureate(s) is located; age of laureate / average age of laureates. During the years 1969–2022, the prize was awarded 54 times, and 92 people became its laureates. 74 awardees are affiliated with North America. The age of most scientists at the time of awarding was from 61 to 70 years. According to the obtained forecast trend models, in 2023 the laureates of the prize will be 3 people, representatives of higher education institutions from North America, whose average age will be in the range of 61–70 years.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".