Using conceptual depth criteria: addressing the challenge of reaching saturation in qualitative research
Bibliographic record
Abstract
Saturation remains a problematic concept within the field of qualitative research, particularly with regard to issues of definition and process. This article sets out some of the common problems with saturation and, with reference to one research study, assesses the value of adopting a range of ‘conceptual depth criteria’ to address problems of definition and process when seeking to establish saturation within a grounded theory approach. It is suggested that the criteria can act as a test to measure the progress of the theoretical sampling and thus ascertain the readiness of the research for the final analytical stages and theory building. Moreover, the application of ‘conceptual depth criteria’ provides the researcher with an evaluative framework and a tool for producing a structured evidence base to substantiate choices made during the theoretical sampling process.
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.
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 | 0.556 | 0.666 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.009 | 0.010 |
| 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".