Improving Qualitative Research Findings Presentations
Bibliographic record
Abstract
Every year thousands of presentations of qualitative research findings are made at conferences, departmental seminars, meetings, and student defenses. Yet scant scholarship has been devoted to these presentations, their nature and relevance to qualitative research, and how they can be improved. This article addresses this important gap by positioning “research findings” presentations as a distinctive genre, part of qualitative method, and an expression of scholarly discourse. From the theoretical basis of genre theory, a number of common and damaging mistakes are found to be evident in the manner in which qualitative research findings are usually presented. These have negative implications: reducing the methodological quality of, engagement with, and overall influence of the qualitative research presented. We draw on genre theory to make recommendations for future qualitative research findings presentations to improve the rigor, influence, and impact of such presentations.
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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 | 0.411 | 0.713 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".