Engaging with Phenomenology: Is it more of a Challenge than it Needs to be?
Bibliographic record
Abstract
When seeking to understand the human condition, with all the problems this enterprise poses for traditional scientific research approaches, qualitative research is held to be in some ways superior to rigidly quantitative research. As a result, many beginning health researchers plan to employ a qualitative approach to explore topics that were previously inaccessible via traditional scientific means. However, implementing a qualitative approach is not an easy process and, in many cases, researchers must look long and hard to find material to assist them in developing their research plans. This may be particularly so in phenomenological research. This article examines some of the problems and pitfalls faced by phenomenological researchers new to the approach. Through accounts of personal experience, it highlights some of the areas where phenomenological researchers could be helpful by being less reticent about the process of implementing a phenomenological study.
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 | 0.256 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.023 | 0.128 |
| Scholarly communication | 0.035 | 0.056 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".