What would I change the next time? A confessional tale of in‐depth qualitative data collection
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
Purpose The purpose of this paper is to discuss how fieldwork impacted the author's own and one participant's positioning; the author's reflexivity, experiences and feelings of alterity; the participant's performances and conversations between the author and participant. Design/methodology/approach The author uses a confessional tale to describe the time spent with the participant and confesses how it impacted on the author as the researcher. The author examines her biases, feelings, and vulnerabilities, and explores some of the methodological and positioning issues with which she struggled. Findings The author ponders on what she learned while being in such close quarters with a participant and discusses what she should keep in mind about herself as the researcher during subsequent data collection forays. Researchers should know themselves well before attempting such closeness because when we are researchers, we can’t change who we are as people. Originality/value It is believed that the extreme researcher/participant closeness was unique but was, at the same time, an extremely useful form of data collection.
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.
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 | 0.271 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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