Bridging Conceptions of Quality in Moments of Qualitative Research
Bibliographic record
Abstract
Quality assessment in qualitative research has been, and remains, a contentious issue. The qualitative literature contains a diversity of opinions on definitions of and criteria for quality. This article attempts to organize this diversity, drawing on several examples of existing quality criteria, into four main approaches: qualitative as quantitative criteria, paradigm-specific criteria, individualized assessment, and bridging criteria. These different approaches can be mapped onto the historical transitions, or moments, in qualitative research presented by Denzin and Lincoln and, as such, they are presented alongside the various criteria reviewed. Socio-political conditions that have led us to a fractured future, where the value and significance of qualitative work may be marginalized, support the adoption of bridging criteria. These broadly applicable criteria provide means to assess quality and can be flexibly applied among the diversity of qualitative approaches used by researchers. Five categories that summarize the language used within bridging criteria are presented as a means to move forward in developing an approach to quality assessment that fosters communication and connections within the diversity of qualitative research, while simultaneously respecting and valuing paradigmatic and methodological diversity.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.404 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.014 | 0.102 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".