Unprejudice as an Approach to Subjective Reality. The Hermeneutic Phenomenological Methodology
Bibliographic record
Abstract
Abstract This chapter presents a comprehensive overview of hermeneutic phenomenological methodology, emphasizing its value in capturing the complexity and richness of subjective human experience. Rooted in the philosophical contributions of Husserl, Heidegger, Gadamer, and others, this approach integrates phenomenological reduction with hermeneutic interpretation to explore lived experiences in depth. The chapter outlines key principles such as epoché, noesis-noema structures, and the fusion of horizons, highlighting their relevance in qualitative research. It further details contemporary methodological adaptations—including Van Manen’s poetic inquiry, Giorgi’s descriptive steps, Petitmengin’s microphenomenology, and Smith and Osborn’s Interpretative Phenomenological Analysis—demonstrating how each contributes unique tools for accessing and interpreting experiential meaning. Practical aspects such as research design, data collection, informed consent, and interview strategies are examined, showing how this methodology fosters an ethical, dialogic, and reflective approach to inquiry. Ultimately, hermeneutic phenomenology is portrayed as both a rigorous and flexible methodology that respects the individuality of experience while seeking universal insights about the human condition.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".