A Methodological and Practical Guide to Study Peripheral Voices in Qualitative Research
Bibliographic record
Abstract
The selection of the voices that make up a qualitative research project is of great importance since the knowledge gained about a certain object of study depends on it. Qualitative researchers focus on the voices around which the purposes of their research explicitly revolve. However, they do not customarily pay attention to peripheral voices, which are primordial to understanding the complexity of the phenomenon studied. In this article, we fill in the literature gap regarding the inclusion of peripheral voices as participants in qualitative research. We develop a five-stage methodological and practical guide to identify who the peripheral voices are, how to plan their approach, how to listen to them and how to analyze their narratives. We illustrate its practical application using the results of doctoral research focused on the construction of the nurse’s status in healthcare organizations. We conclude by discussing the potential of their inclusion in qualitative research.
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How this classification was reachedexpand
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 | 0.536 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".