Bibliographic record
Abstract
Phenomenological researchers generally agree that our central concern is to return to embodied, experiential meanings aiming for a fresh, complex, rich description of a phenomenon as it is concretely lived. Yet debates abound when it comes to deciding how best to carry out this phenomenological research in practice. Confusion about how to conduct appropriate phenomenological research makes our field difficult for novices to access. Six particular questions are contested: (1) How tightly or loosely should we define what counts as "phenomenology" (2) Should we always aim to produce a general (normative) description of the phenomenon, or is idiographic analysis a legitimate aim? (3) To what extent should interpretation be involved in our descriptions? (4) Should we set aside or bring to the foreground researcher subjectivity? (5) Should phenomenology be more science than art? (6) Is phenomenology a modernist or postmodernist project, or neither? In this paper, I examine each of these areas of contention in the spirit of fostering dialogue, and promoting openness and clarity in phenomenological inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.537 | 0.483 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.014 | 0.097 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".