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Record W2969190838 · doi:10.1152/advan.00079.2019

Answering Huxley: “now” students take a “then” exam

2019· article· en· W2969190838 on OpenAlexafffundabout
P. K. Rangachari

Bibliographic record

VenueAJP Advances in Physiology Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersMcMaster University
KeywordsSet (abstract data type)PsychologyMedical educationPhysiologyMathematics educationMedicineComputer science

Abstract

fetched live from OpenAlex

Twenty-eight undergraduate students in a health sciences program volunteered for an exercise in the history of examinations. They had completed a second-year course in anatomy and physiology in which they studied modern texts and took standard contemporary exams. For this historical “experiment,” students studied selected chapters from two 19th century physiology texts (by Foster M. A Textbook of Physiology, 1895; and Broussais FJV. A Treatise on Physiology Applied to Pathology, 1828). They then took a 1-h-long exam in which they answered two essay-type questions set by Thomas Henry Huxley for second-year medical students at the University of London in 1853 and 1857. These were selected from a question bank provided by Dr. P. Mazumdar (University of Toronto). A questionnaire probed their contrasting experiences. Many wrote thoughtful, reflective comments on the exercise, which not only gave them an insight into the difficulties faced by students in the past, but also proved to be a valuable learning experience (average score: 8.6 ± 1.6 SD).

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0420.017

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 head0.014
GPT teacher head0.294
Teacher spread0.281 · 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 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

Citations0
Published2019
Admission routes3
Has abstractyes

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