Gadamerian Hermeneutics and Feminist Thought: Exploring Preunderstandings to Uncover Experiences of Prejudice
Bibliographic record
Abstract
German philosopher Hans-Georg Gadamer is best known for his contribution to the development of philosophical hermeneutics, an interpretive approach to knowledge, understanding and meaning-making. It has become a well-established research approach in the health sciences to shed light on the lived experiences of people living with challenging chronic health conditions. Some feminist scholars have gravitated to Gadamer’s hermeneutics for its steadfast rejection of positivism and its intention to uncover preunderstandings and prejudices. However, others have critiqued the approach for its lack of focus on prescribing action for social change and its reluctance to evaluate the prejudices present in its own tradition. In this paper, the authors will demonstrate how using feminist hermeneutics can help health researchers deepen their understanding of illness narratives by examining the power structures contributing to the marginalization of chronically ill people within and outside the healthcare system. They will juxtapose a reflexive investigation of the first author’s experiences with a focused literature review of the dialogue between hermeneutics and feminism. By examining the first author’s experiences with Gadamerian hermeneutics and feminist hermeneutics through self-study, she can in turn unearth her own preunderstandings. This approach will allow the authors to leverage the depth of interpretive understanding generated by hermeneutics while exploring the power structures involved in the complex process that is patient care, particularly that of people with chronic illness. They conclude that this combined approach of feminist hermeneutics allows health researchers to deepen their understanding of illness narratives with issue-specific and effective recommendations to clinicians and public health officials, leading to better-adapted services through a more just approach to chronically ill people.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.097 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".