Choosing an Analytical Approach in Phenomenological Inquiry
Bibliographic record
Abstract
Phenomenology as a research methodology is based on Edmund Husserl's and Martin Heidegger's philosophy of phenomenology, which addresses the subject of human experience. It is one of the commonly used methodologies in health sciences research. This second editorial in the series titled "Focus on Qualitative Data Analysis" aims to provide researchers with guidance on how to choose appropriate methods of analysis among varied phenomenological approaches. We provide an analytical choice tree that presents our perspective on the methods of analysis in phenomenology. The previous article in this series addressed case study methodology. Future articles will address qualitative description, grounded theory, and narrative inquiry.
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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 | 0.090 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".