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Record W4295957315 · doi:10.1109/mts.2022.3197273

Folkmedical Technologies and the Sociotechnical Systems of Healthcare

2022· article· en· W4295957315 on OpenAlexaff
Jordan Richard Schoenherr

Bibliographic record

VenueIEEE Technology and Society Magazine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsCarleton UniversityConcordia University
Fundersnot available
KeywordsSociotechnical systemReciprocity (cultural anthropology)Health careKnowledge managementHealthcare systemHealth professionalsSocial exchange theoryPublic relationsBusinessComputer sciencePsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Trust is necessary for any kind of complex social organization. Whether trust is the result of past exchanges with a specific individual (e.g., direct reciprocity) or the result of norms and conventions within a social network (e.g., indirect reciprocity), understanding the basis for trust is a prerequisite for successful coordination between agents. Nowhere is this more evident than in the healthcare setting. Traditionally, exchanges in healthcare occur between healthcare professionals (HCPs, e.g., doctors, nurses) and patients or parents.

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)0.004
metaresearch head score (Gemma)0.008
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.021
Scholarly communication0.0100.009
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.295
Teacher spread0.278 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

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Same venueIEEE Technology and Society MagazineSame topicMisinformation and Its ImpactsFrench-language works237,207