Narrative Inquiry: A Critical Examination of Its Theoretical Foundations and Methodological Applications
Bibliographic record
Abstract
This review article offers an in-depth examination of narrative inquiry, a methodological approach dedicated to exploring individual stories to gain insights into their experiences. The article begins by introducing narrative inquiry, elucidating its origins, significance, and applicability across various disciplines. It then explores narrative inquiry as a research design, highlighting its capacity to facilitate an in-depth exploration of personal and social narratives. The review proceeds to outline the procedural steps involved in conducting narrative research, encompassing the formulation of research questions, data collection, and the interpretation of narrative data. Following this, the article details the methods for data analysis within narrative inquiry, emphasizing the critical roles of contextual and thematic analysis in revealing underlying meanings. The discussion concludes with an examination of ethical considerations pertinent to narrative inquiry, emphasizing the importance of maintaining confidentiality and privacy for participants. Through this comprehensive review, the article aims to furnish researchers and scholars with a robust understanding of narrative inquiry, including its methodological underpinnings and practical applications.
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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 | 0.074 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".