Initiating a Grounded Theory Study: Scoping the Area of Interest, Overcoming Hurdles in the Ethics Review, and Initial Data Collection
Bibliographic record
Abstract
A well-executed grounded theory study requires thoughtful planning coupled with an awareness that grounded theory research rests on a foundation of emergence and openness to where the data leads the investigator. Grounded theory allows for multiple sources of data that offer insight into the topic and aid in theory development. Scooping the area of interest offers an opportunity to explore diverse sources where data can be found and lays the foundation for writing a successful ethics application. Writing a grounded theory ethics application entails overcoming hurdles such as, navigating how to formulate the research question so it is sufficiently open to allow for what emerges during the study as important to the participants, estimating sample size when this cannot be known beforehand in grounded theory, and providing a list of and rationale for data sources. This article offers insights into how to scope the area of interest, guidance on how to complete an ethics application, and advice on how to initiate data collection with special attention given to conducting interviews and observations.
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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 | 0.387 | 0.447 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".