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Record W2998363599 · doi:10.1097/acm.0000000000003142

Philosophy of Science Series: Harnessing the Multidisciplinary Edge Effect by Exploring Paradigms, Ontologies, Epistemologies, Axiologies, and Methodologies

2019· article· en· W2998363599 on OpenAlexaff
Lara Varpio, Anna MacLeod

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMultidisciplinary approachOntologyEpistemologyEngineering ethicsGenerative grammarGRASPRhetoricAxiologyPhilosophy of scienceSociologyComputer scienceData scienceSocial scienceArtificial intelligencePhilosophyEngineering

Abstract

fetched live from OpenAlex

Health professions education (HPE) researchers come from many different academic traditions, from psychology to engineering to rhetoric. Trained in these traditions, HPE researchers engage in science and the building of new knowledge from different paradigmatic orientations. Collaborating across these traditions is particularly generative, a phenomenon the authors call the multidisciplinary edge effect. However, to harness this productivity, scholars need to understand their own paradigms and those of others so that collaboration can flourish. This Invited Commentary introduces the Philosophy of Science series-a collection of articles that introduce readers to 7 different paradigms that are frequently used in HPE research or that the authors suggest will be increasingly common in future studies. Each article in the collection presents a concise and accessible description of the main principles of a paradigm so that researchers can quickly grasp how these traditions differ from each other. In this introductory article, the authors define and illustrate key terms that are essential to understanding these traditions (i.e., paradigm, ontology, epistemology, methodology, and axiology) and explain the structure that each article in this series follows.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.122
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.122
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.007
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.146
GPT teacher head0.406
Teacher spread0.260 · 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 teacher head, not a consensus.

Study designObservational
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

Citations119
Published2019
Admission routes1
Has abstractyes

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