Assessing the FACTS: A Mnemonic for Teaching and Learning the Rapid Assessment of Rigor in Qualitative Research Studies
Bibliographic record
Abstract
Teaching and learning research appraisal strategies is a challenge in undergraduate education and for practitioners alike. The appraisal of rigor in qualitative research papers is particularly complex and sophisticated work for many undergraduate research students and practitioners who want to develop their critical reading skills. The mnemonic strategy (The FACTS) explained in this paper is one pedagogical strategy for establishing a simplified approach to teaching and learning the appraisal of rigor in qualitative research. While not a comprehensive tool, the FACTS are a useful introduction to the complex challenge of qualitative research appraisal.
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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 | 0.200 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".