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Record W2772810774 · doi:10.1080/10401334.2017.1392864

HPE as a Field: Implications for the Production of Compelling Knowledge

2017· article· en· W2772810774 on OpenAlexaff
A. van Enk, Glenn Regehr

Bibliographic record

VenueTeaching and Learning in Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBlueprintDisciplinePublic relationsField (mathematics)Privilege (computing)NegotiationEngineering ethicsSociologyCompromisePolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

ISSUE: Research in education, including health professions education, has long struggled with the competing concerns of academic and practice-based stakeholders. Inspired partially by the work of Stokes and other theorists in science and technology studies, we propose that discussions about compelling research in health professions education might be usefully advanced by considering what it would mean if the community framed itself as a knowledge-producing field instead of aligning itself with either disciplinary or practical interests. EVIDENCE: Efforts to foreground disciplinary or practical interests in education research have been unproductive, leading to the privileging of one group's expertise at the expense of the other. Currently proposed principles and practices for responding to the divergence between these interests, such as knowledge translation or practitioner inquiry, have yielded comparatively little in the way of mutual satisfaction. IMPLICATIONS: As a field, health professions education research would not privilege either disciplinary or practical interests, nor would it attempt any sort of definitive blueprint for resolution to the tension. Rather it would regard these interests as inherently interconnected and, therefore, always in tension to varying degrees. The challenge for a field is not to resolve that tension but to harness it in productive ways through collaboration, negotiation, and compromise, through ever-shifting engagements that will not necessarily be comfortable but will nonetheless foster knowledge that resonates with all parts of the community.

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)0.153
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.153
Threshold uncertainty score0.807

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1530.147
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0180.168
Scholarly communication0.0360.065
Open science0.0060.029
Research integrity0.0230.021
Insufficient payload (model declined to judge)0.0150.003

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 head0.047
GPT teacher head0.424
Teacher spread0.377 · 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 designTheoretical or conceptual
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

Citations47
Published2017
Admission routes1
Has abstractyes

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