Using Criteria of Significance to Make Sense of Data: Implications for Qualitative Research
Bibliographic record
Abstract
For many qualitative researchers, the task of dealing with huge amounts of data can be overwhelming. In many qualitative research methodologies, procedures for making sense of large amounts of data are often intentionally unclear and open to interpretation due to the wide range of variability of data and research context. This can be problematic for novice and experienced researchers alike as they consider what parts of their data to feature, exemplify and draw conclusions from. This article puts forth a construct that makes explicit the logics of two researchers using what they label as “criteria of significance” to make sense of their qualitative data. The Criteria of Significance (CoS) serves as a defensible set of criteria by which data is given increased or decreased value regarding its use in the final analysis and conclusions drawn from a study. This paper examines two qualitative studies (Hirschkorn, 2008; Morrison, 2018) and explores how CoS was used to differentiate the data used in their findings.
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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 | 0.675 | 0.803 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.020 | 0.120 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.009 | 0.017 |
| 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, 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".