Numerically Aided Phenomenology: Procedures for Investigating Categories of Experience
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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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 | 0.070 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".