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Record W4285613944 · doi:10.1177/16094069221101237

Applied Philosophical Hermeneutic Research: <i>the unmethod</i>

2022· article· en· W4285613944 on OpenAlexaff
Richard Hovey, Marie Vigouroux, Nioushah Noushi, Veeresh Pavate, Kristina Amja

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMontreal Children's HospitalMcGill University
Fundersnot available
KeywordsHermeneuticsSociologyPhilosophical methodologyEpistemologyPhilosophical theoryPsychologyPhilosophy

Abstract

fetched live from OpenAlex

The purpose of this article is to provide insight into the philosophy and practice of Applied Philosophical Hermeneutics as a unique approach for research in the health sciences. While there are other research approaches grounded in hermeneutics, this article focuses on Gadamer’s modern philosophical hermeneutics. During my 18 years as a hermeneutic researcher, graduate students and I have engaged with people with a wide variety of experiences such as preventable medical error, Indigenous health, adult and adolescent chronic pain, social responsibility in higher education, oral health care for autistic children, and the experiences of people living with Thalassemia. Applied Philosophical Hermeneutics offers an approach to help bring researchers, clinicians, and patients together within a community of active partnerships in research. All these projects employed Applied Philosophical Hermeneutics as an approach to gain a deeper and personal understanding of the unique experiences of these diverse groups of people. Hermeneutics remains somewhat confusing as the unmethod which may lead to a dismissive attitude toward this research approach. Therefore, the intention of this paper is to present a risk-free insight into hermeneutics, which hopefully will open-up conversations and new learning experiences among researchers, students, patients, and colleagues.

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)0.041
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.744
GPT teacher head0.687
Teacher spread0.057 · 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 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

Citations23
Published2022
Admission routes1
Has abstractyes

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