Husserlian Descriptive Phenomenology: A review of intentionality, reduction and the natural attitude
Bibliographic record
Abstract
Background and aim: Descriptive phenomenology is widely used in social science research as a method to explore and describe the lived experience of individuals. It is a philosophy and a scientific method and has undertaken many variations as it has moved from the original European movement to include the American movement. The aim of this paper is to describe descriptive phenomenology in the tradition of Edmund Husserl. Integrative literature discussing the nature of descriptive phenomenology was used within this paper to elucidate the core fundamental principles of Husserlian descriptive phenomenology.Methods: This is a methodology paper that provides both an overview of the historical context and the development of descriptive phenomenology in the tradition of Husserl.Results and discussion: Descriptive phenomenology is explained from its historical underpinnings. The principles of the natural attitude, intentionality and the phenomenological reduction are described and using practical examples illustrate how each of these principles is applied within a research context.Conclusions: Understanding the key philosophical foundations of Husserlian descriptive phenomenology as a research method can be daunting to the uninitiated. This paper adds to the discussion around descriptive phenomenology and will assist and inform readers in understanding its key features as a research method.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".