What would I change the next time? A confessional tale of in‐depth qualitative data collection
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.
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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 | 0.109 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".