A Chat with Patricia Leavy on “RE/INVENTION: METHODS OF SOCIAL FICTION” BY PATRICIA LEAVY (2023)
Bibliographic record
Abstract
Within this conversation, Patricia Leavy discusses her new book Re/Invention: Methods of Social Fiction. As a leading artful scholar, Leavy shines a light on the slippage between fiction and nonfiction, and the long history of merging scholarship with the literary arts. Within the paradigm of arts-based research, Leavy views social fiction as a method. She speaks about how crafting academic fiction allows for research to become both an act of discovery and a pathway toward personal healing. Christina Flemming is delighted to hear more about Leavy’s daily writing practice, her thoughts on writing as rewriting, and the metaphorical blender that is required to take one’s lived experiences and transform them into academic fiction. You can learn more about Re/Invention: Methods of Social Fiction via the Guilford Press website. To read more about Patricia Leavy, visit her website: https://patricialeavy.com/
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".