Performing excellence: Nobel Prize nomination networks in North America
Bibliographic record
Abstract
This paper examines how scientific excellence is performed in Nobel nominations for medical scientists. Performing excellence encompasses both conducting excellent scientific work and being recognized for it. Both dimensions are closely intertwined: doing and recognizing excellent work depend on each other. Tracing nominations from the Nobel Archives in Solna, Sweden, the paper shows that Nobel Prizes are only the tip of the iceberg of networks of scientific recognition, which belong to cultures of excellence. Approaching cultures of excellence through nominations helps to understand how scientific prizes were awarded. The nominations show that science is not just a cognitive activity but also a social endeavour, and that the decision about who is awarded the Nobel Prize is also an outcome of social processes. Analysing the nomination networks thus explains to a certain extent the predominance of researchers from the USA versus Canada (and other countries). It shows, among other things, that a proactive policy of Nobel Prize nominations is part of the culture of excellence in which American scientists often participate. The mechanisms of scientific recognition as reflected in Nobel Prize nomination networks and rhetoric give insight into the patterns and the background of awarding the prize.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".