Numerically Aided Phenomenology: Procedures for Investigating Categories of Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complementarity between quantitative and qualitative methods often implies that qualitative methods are a step toward quantitative precision or that quantitative and qualitative methods provide mutually validating "triangulation." However, there also is unacknowledged quantification within the type of analytic induction that is considered pivotal in qualitative thinking. We attempt to justify this claim and present a form of phenomenological analysis that invokes numeric algorithms. Numerically aided phenomenology is a procedure for systematically describing categories (kinds, or types) of lived experience within a set of experiential narratives. In a comparative reading, recurrent meaning expressions are identified and paraphrased. Then judgments about their presence or absence are used to create matrices representing the profiles of meanings expressed in each narrative. Finally, cluster analytic algorithms are used to group these experiential narratives according to the similarities in their profiles of meaning expressions. In this way, categories of similar experiential narratives—and their distinctive attributes—can be identified. Rather than an essentialist conception of the qualities defining classes, in numerically aided phenomenology classes are defined by more-or-less invariant attributes, i.e., classes are formed such that members share a large number of expressed meanings, although no single meaning (or set thereof) is necessary or sufficient for class membership. URN: urn:nbn:de:0114-fqs0101153
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it