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Record W2323698985 · doi:10.1097/acm.0b013e3181f56213

A “Paradise for Scholars”: Flexner and the Institute for Advanced Study

2010· article· en· W2323698985 on OpenAlexaboutno aff
Richard B. Gunderman, Kelly Gascoine, Frederic W. Hafferty, Steven L. Kanter

Bibliographic record

VenueAcademic Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsParadisePolitical scienceLibrary scienceMedical educationSociologyMedicineHistoryArt history

Abstract

fetched live from OpenAlex

The story of Abraham Flexner's role in the founding of the Institute for Advanced Study in Princeton, New Jersey, offers important insights into Flexner himself and valuable lessons for contemporary leaders in academic medicine. The authors review this remarkable achievement along with Flexner's other accomplishments in the 49 years he lived after the 1910 publication of his report on medical schools in the United States and Canada. Lessons include his approach to philanthropy, his determination and networking, his belief in the synergism between the natural sciences and the humanities, and his regard for learning as a lifelong process. Flexner's record was not unblemished, however, and the cases in which he faltered also offer important lessons for today's academic leaders.

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)0.010
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0190.030
Scholarly communication0.0200.022
Open science0.0020.013
Research integrity0.0080.017
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.032
GPT teacher head0.418
Teacher spread0.387 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2010
Admission routes1
Has abstractyes

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