Are Grounded Theory and Action Research Compatible? Considerations for Methodological Triangulation
Bibliographic record
Abstract
This paper explores the prospects of combining Grounded Theory (GT) and Action Research (AR) methodologies to spark further methodological discussion. GT and AR methodologies are sometimes used together in the same study without a discussion of their methodological compatibility. However, different iterations of GT and various forms of AR may inform the level of mutual compatibility. The goal of this conceptual paper is to answer two questions: Which iteration of GT could be more compatible with which form of AR? What benefits and challenges would such a methodological combination pose? The author presents a brief comparative review of GT and AR approaches, commenting on the intriguing complementarities of these methodologies and the benefits of their triangulation in social research. The author concludes that, although the prospect of combining GT and AR is promising, it undeniably requires further scrutiny in the applied research.
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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 | 0.741 | 0.762 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.017 | 0.124 |
| Scholarly communication | 0.048 | 0.088 |
| Open science | 0.014 | 0.043 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".