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Record W4387129544 · doi:10.3138/cjhh.598-072022

J.J.R. Macleod: Misunderstood, Misinterpreted, and Maligned

2023· article· en· W4387129544 on OpenAlexaffabout
James R. Wright

Bibliographic record

VenueCanadian Journal of Health History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and modern epidemiology studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBLISSMythologyReputationHistoryPsychoanalysisClassicsArt historyPsychologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Abstract. After the discovery of insulin at the University of Toronto in 1921–22, Frederick Banting and Charles Best downplayed the contributions of physiology professor John James Rickard Macleod, the director of the laboratory where the discovery was made. Banting and Best, their allies, and to a lesser extent the university promoted a “fairy tale” version in which the two young investigators made the discovery on their own, creating the so-called “Banting and Best myth.” Over the next 60 years, the myth prevailed and Macleod's reputation became increasingly tarnished, with both Banting and Best actively maligning their former mentor. While the publication of Michael Bliss’ The Discovery of Insulin in 1982 placed Macleod's reputation on the road to recovery, there are still many lingering issues that have been raised, and Macleod remains misunderstood, misinterpreted, and maligned. This paper, using primary and secondary historical sources, addresses topics that have been repetitively raised by Macleod's detractors over the past century.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0060.022
Scholarly communication0.0080.010
Open science0.0020.003
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.127
GPT teacher head0.341
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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