Fiction as Research Practice: Short Stories, Novellas, and Novels (2013) by Patricia Leavy
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
In the first section, Patricia Leavy explores the genre by explaining its background and possibilities and goes on to describe how to conduct and evaluate fiction-based research.In the second section of the book, she presents and evaluates examples of fiction-based research in different forms including short stories and excerpts from novellas and novels written by different authors.The third and final section explains how fiction and fiction-based research can be used in teaching.Leavy clearly differentiates the term fiction-based research from artsbased research in order to project the emergent field in a clear light of its own.Babbie (2001) explains that just as qualitative research practice emerged as a means of explaining phenomena that could not be captured by quantitative scientific research, social research attempts to study and understand everyday life experiences.Within social research, arts-based research tries to represent phenomena studied aesthetically through various forms of art (Barone & Eisner, 2012).As a form of arts-based research, Leavy describes fiction-based research as a great way to explore "topics that can be difficult to approach" through fiction (p.20).Topics include the intricacies of interactions in everyday life, race relations, and socio-economic class and its effects on human life.In carving its niche in social research, Leavy explains that fiction-based research seeks to create a deeper understanding of experiences in a language that is more accessible to people than research published in academic publications.Using fiction creates an opportunity for the writer to simulate the environment, sights, sounds, and smells of reality virtually, which captivates the reader's imagination.The writer is able to either create new knowledge for the reader or "disrupt dominant ideologies or stereotypes" (p.38).As traditional qualitative researchers, fiction writers engage in intensive research to ensure that they have clear representations of the phenomenon they are presenting.These representations are evident in the realistic scenarios and characters that are portrayed in fiction writing, allowing the reader to be absorbed in the reality of the book.This reality or verisimilitude is the key to effective fictionbased research and traditional qualitative research because both methods try to portray the experiences as true as possible.In describing how one conducts fiction-based research, Leavy compares tenets of qualitative research to those of fiction-based research.She points out that anticipated data is a key consideration in most qualitative research methods but how data is collected, where it is
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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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it