Applied Philosophical Hermeneutic Research: <i>the unmethod</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.230 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".