Grounded theory: reflections on the emergence vs. forcing debate
Bibliographic record
Abstract
AIM: The aim of this paper is to compare Glaser's model of theory generation, where theory rises directly and rigorously out of the data, devoid of interpretivism, to Strauss's conceptually descriptive approach that encourages directive questioning and supports an interpretive stance. BACKGROUND: The discovery of grounded theory (GT) was born out of a merger between Barney Glaser and Anselm Strauss, the proverbial 'fathers' of GT. Since the co-creation of their approach to theory development through research in 1967, these scholars have taken seemingly divergent paths in further developing and evolving the pragmatic use of GT. DISCUSSION: Numerous researchers have used GT as a general method, applying it to both quantitative and qualitative research approaches. In this paper we discuss the stages and strategies of data sampling, collection, coding and analysing used by both Glaser and Strauss. Constant comparative analysis is identified as the primary strategy in the integrated coding and analysing stages of this theorizing method, regardless of the researcher's philosophical or research orientation. We also discuss initial or open coding, advanced coding, memoing, and theoretical sampling, with particular attention to comparing and contrasting the descriptive terms and application strategies that have been suggested by both Glaser and Strauss. CONCLUSION: The reported distinctions in the approach, method, and general intent of GT reflected in this paper are not easy to comprehend. The two methods reflect different basic philosophical paradigms, and therefore represent distinct approaches to GT. Researchers need to be clear about which philosophy and resulting analysis approach they are using, and the effect that approach will have on the research process and outcomes.
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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 | 0.281 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.013 | 0.207 |
| Scholarly communication | 0.033 | 0.051 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 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".