Using Criteria of Significance to Make Sense of Data: Implications for Qualitative Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it