Fiction as Research Practice: Short Stories, Novellas, and Novels (2013) by Patricia Leavy
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 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.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".