Developing Sampling Frame for Case Study: Challenges and Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to statistical analysis, the issue of random sampling is pertinent to any quantitative study. Unlike quantitativestudy, the elimination of inferential statistical analysis, allows qualitative researchers to be more creative in dealingwith sampling issue. Since results from qualitative study cannot be generalized to the bigger population, qualitativeresearchers do not have to endure the strenuous randomization process of sampling procedure. However, qualitativeresearchers should not take sampling procedures too lightly, and if they do, it will affect the richness and theappropriateness of the data. The chances are, the data will not answer their research questions and this can frustratethe researchers when making meanings to the data. This paper will examine the available methods in samplingparticipants for qualitative study. Specifically, the paper will discuss the sampling frame suitable for case study, suchas single-case (holistic and embedded), multi-case, and a snowball or network sampling procedure. Discussion willalso involve challenges anticipated for each procedure.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it