Bibliographic record
Abstract
This book is an essential guide for students, researchers, and practitioners involved in qualitative research across various disciplines. It explores the philosophy, methodology, and practical aspects of qualitative research, offering a robust framework for understanding and conducting high-quality qualitative studies. It will cover a range of qualitative methods, including case studies, ethnography, grounded theory, narrative analysis, and phenomenology, providing readers with the tools they need to select and apply the appropriate methods for their research questions. The primary aim of this book is to demystify qualitative research by providing clear, accessible, and comprehensive guidance on various qualitative methods. It seeks to equip readers with the knowledge and skills needed to design, conduct, and analyse qualitative research effectively. By integrating theoretical insights with practical examples and case studies, it aims to bridge the gap between theory and practice, fostering a deeper understanding and appreciation of qualitative research. This book will provide an excellent reference for students and researchers across multiple disciplines, as well as those who are new to using qualitative methods and wish to familiarise themselves with a comprehensive overview of the research methods.
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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 | 0.059 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".