Bibliographic record
Abstract
In this article, I try to think through the question, "What distinguishes phenomenology in its original sense?" My intent is to focus on the project and methodology of phenomenology in a manner that is not overly technical and that may help others to further elaborate on or question the singular features that make phenomenology into a unique qualitative form of inquiry. I pay special attention to the notion of "lived" in the phenomenological term "lived experience" to demonstrate its critical role and significance for understanding phenomenological reflection, meaning, analysis, and insights. I also attend to the kind of experiential material that is needed to focus on a genuine phenomenological question that should guide any specific research project. Heidegger, van den Berg, and Marion provide some poignant exemplars of the use of narrative "examples" in phenomenological explorations of the phenomena of "boredom," "conversation," and "the meaningful look in eye-contact." Only what is given or what gives itself in lived experience (or conscious awareness) are proper phenomenological "data" or "givens," but these givens are not to be confused with data material that can be coded, sorted, abstracted, and accordingly analyzed in some "systematic" manner. The latter approach to experiential research may be appropriate and worthwhile for various types of qualitative inquiry but not for phenomenology in its original sense. Finally, I use the mythical figure of Kairos to show that the famous phenomenological couplet of the epoché-reduction aims for phenomenological insights that require experiential analysis and attentive (but serendipitous) methodical inquiry practices.
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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 | 0.037 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".