First among Equals: Macleod, Banting, and the Discovery of Insulin in Toronto
Bibliographic record
Abstract
The discovery of insulin in Toronto in 1921/1922 was rapidly recognized by a Nobel Prize shared by Dr. Frederick Banting and Prof. John Macleod. However, in the popular imagination it is attributed to “Banting and Best.” Here we review the important individual and collective contributions of the four main players in Toronto at that time: Banting, Macleod, Best, and Collip. Drawing on the work of the late Canadian historian Michael Bliss, we reflect on some of the enduring myths that surround the tale. We question the romantic notion that Banting’s inspirational “idea” led directly to the discovery, and argue that it can instead be attributed to a fortunate collision of opportunity, ability, experience, and drive. We go on to recount the clashes of personality that detracted from the celebration that should have marked the delivery of insulin to the world. Finally, we set out evidence that Banting consistently overestimated his own contribution, conducting a successful and enduring campaign to downplay the roles of his colleagues. In the hope of redressing the historical balance, we focus on the neglected role of John Macleod, an established international expert on carbohydrate metabolism at the time of the discovery, who directly supervised the work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".