Who Do We Think We Are? Disrupting Notions of Quality in Qualitative Research
Bibliographic record
Abstract
The purpose of this article is to illuminate our troubles with, and troubling of, the trustworthiness dimension of balancing subjectivity and reflexivity, in qualitative research. This article evolved from debriefing sessions between three novice researchers working on a qualitative research study aimed at building understandings of the relational dynamics between adults with developmental disability diagnoses (ADevD) and their caregiving families. Following data collection, coauthors discussed interview experiences they had personally found challenging. These experiences constitute a point of departure for our examination of our researcher positions. We present a delineation of three research tensions, in the form of short "reflexive vignettes," each rooted in concern with possibly contradicting our goals of facilitating and expanding participant autonomy. We follow with recommendations about how, as researchers, our endeavor to understand participants with less conventional communication can be used to reflect and inform navigating difficulties universal to qualitative research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.717 | 0.644 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.027 | 0.226 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".