The Relational Ethics of Narrative Inquiry
Bibliographic record
Abstract
"Narrative inquiry is based on the proposition that experience is the stories lived and told by individuals as they are embedded within cultural, social, institutional, familial, political, and linguistic narratives. It represents the phenomenon of experience but also constitutes a methodology for its study. At the heart of this methodology is relational ethics. However, until now the functioning of this key relationship in practice has remained largely undefined. In this book the authors take on the essential task of developing a conceptual framework for the application of relational ethics to narrative inquiry. Building on a corpus of more generalized research, this book is grounded in a multi-year study with indigenous youth and families. The authors describe their experiences of narrative inquiry, highlighting how relational ethics informed their negotiation of these research relationships. They also engage in a conversation with the work of philosophers who have guided their narrative inquiry to offer a more thorough understanding of relational ethics. Through this, and contributions from five further studies on a diverse range of subjects, a number of key points for successful relational ethics are isolated and expounded upon. This book is an invaluable tool for researchers and postgraduates engaged in qualitative research--providing clear and practical guidance on ethical concerns. It also extends the work of the authors' two previous titles, Engaging in Narrative Inquiry and Engaging in Narrative Inquiries with Children and Youth."--Provided by publisher
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.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".