Merging Historical Feminist Fiction-Based Research With the Craft of Fiction Writing: Engaging Readers in Complex Academic Topics Through Story
Bibliographic record
Abstract
Drawing from the literature and the historical fiction-based feminist antimilitarist research I conducted in writing my debut novel, A Sea of Spectres, this article discusses the what and why of fiction-based research. I detail how to: (a) move from inspiration to fiction-based research; (b) frame the research; (c) develop research questions; (d) and embed theory and data in the story through applying the craft of fiction writing. My aim with fiction-based research is to create compelling characters situated in historical and contemporary settings in order to draw readers into engaging and accessible stories that help them learn about themselves, their understandings of others, and their relationships to society. I conclude with recommendations for conducting fiction-based research; delve into the methodology of fiction-based research; study the craft of writing fiction; read in related genre(s); abide by the ethics of fiction and fiction-based research; and learn about the fiction publishing process.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".