Three principles for writing an effective qualitative results section
Bibliographic record
Abstract
Writing an effective qualitative results section can be a daunting task. How do you report the findings of the study and tell a compelling story? It is this delicate balance that we strive to navigate in this paper. We offer three principles—storytelling, authenticity and argument—to help writers envision the story they will tell, select the data as evidence for that story and integrate quotations to guide the reader’s interpretation. Practical advice and concrete illustrations make the principles easy to apply to your own writing. Finally, by reflecting on how historical, methodological and disciplinary elements shape their application, you will be able to use these principles to enhance the persuasiveness of your qualitative results section.
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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 | 0.544 | 0.597 |
| Meta-epidemiology (narrow) | 0.003 | 0.007 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.015 | 0.037 |
| Insufficient payload (model declined to judge) | 0.019 | 0.020 |
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".