An affirmation of the phenomenological psychological descriptive method: A response to Rennie (2012).
Bibliographic record
Abstract
Rennie (2012) made the claim that, despite their diversity, all qualitative methods are essentially hermeneutical, and he attempted to back up that claim by demonstrating that certain core steps that he called hermeneutical are contained in all of the other methods despite their self-interpretation. In this article, I demonstrate that the method I developed based upon Husserlian phenomenology cannot be so interpreted despite Rennie's effort to do so. I claim that the undertaking of a psychological investigation at large can be considered interpretive but that when the phenomenological method based upon Husserl is employed, it is descriptive. I also object to the attempt to reduce varied theoretical perspectives to the methodical steps of one of the competing theories. Reducing theoretical perspectives to core steps distorts the full value of the theoretical perspective. The last point is demonstrated by showing how the essence of the descriptive phenomenological method is missed if one follows Rennie's core steps.
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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 | 0.044 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.012 | 0.033 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.069 | 0.115 |
| Insufficient payload (model declined to judge) | 0.003 | 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".