Bibliographic record
Abstract
This essay investigates the relationship the Canadian neurosurgeon Wilder Penfield had, in old age, with honours and accolades, including the Nobel Prize. Documents from the Nobel Prize Archives shed light on his nominations and on the assessments the Committee took into consideration, illuminating the professional networks the nomination process activated and the values underwriting the adjudication process. Meanwhile, Penfield's correspondence and personal diary reveal the complex emotions that such a prestigious award can engender. Penfield expressed a reticence to fully embrace the Prize, although he had once actively worked to gather support for his own nomination. This essay also considers a little-studied phenomenon—the rejection of prizes. While mundane considerations such as wishing not to travel may have played a role, Penfield expressed a deeper disconnect between his own sense of self and the prizes he rejected, declaring a feeling of personal unworthiness vis-à-vis their particularities. Moreover, he also expressed a more general ambivalence regarding awards because they tended to single out individuals, and for him this stood in tension with the reality of the collective, communal nature of scientific work and medical practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".