Gadamerian Hermeneutics with Intersectionality as an Analytical Lens
Bibliographic record
Abstract
For decades, hermeneutics has been used as a qualitative research approach to enhance understanding of the experiences of individuals within a particular context. However, after reviewing the literature, it became evident that only a few published articles use intersectionality as an analytical lens along with Gadamerian hermeneutics. This article draws on examples from a 2021 study that explored experiences of LGBTQI+ migrants with healthcare providers. Utilizing the philosophical underpinnings of Gadamerian hermeneutics and the theoretical foundations of intersectionality, the confluences and the tensions between these two approaches is explored. Moreover, suggestions are provided for how intersectionality as an analytical lens can expand understandings and interpretations of research findings using Gadamerian hermeneutics.
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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 | 0.054 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.014 | 0.113 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".