Staying theoretically sensitive when conducting grounded theory research
Bibliographic record
Abstract
Background Grounded theory (GT) is founded on the premise that underlying social patterns can be discovered and conceptualised into theories. The method and need for theoretical sensitivity are best understood in the historical context in which GT was developed. Theoretical sensitivity entails entering the field with no preconceptions, so as to remain open to the data and the emerging theory. Investigators also read literature from other fields to understand various ways to construct theories. Aim To explore the concept of theoretical sensitivity from a classical GT perspective, and discuss the ontological and epistemological foundations of GT. Discussion Difficulties in remaining theoretically sensitive throughout research are discussed and illustrated with examples. Emergence - the idea that theory and substance will emerge from the process of comparing data - and staying open to the data are emphasised. Conclusion Understanding theoretical sensitivity as an underlying guiding principle of GT helps the researcher make sense of important concepts, such as delaying the literature review, emergence and the constant comparative method (simultaneous collection, coding and analysis of data). Implications for practice Theoretical sensitivity and adherence to the GT research method allow researchers to discover theories that can bridge the gap between theory and practice.
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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 | 0.496 | 0.696 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".