Bibliographic record
Abstract
The editors of Narrative Works solicited the five papers of this special issue from major writers and thinkers in the field of narrative. The authors were asked to speak to the importance and role of narrative in their work and discipline, in the hope of somewhat clarifying what it is that narrative is and does, and perhaps providing a basis for seeing links between disciplines and approaches that might further narrative scholarship. In this introduction, I focus on how the five contributions have prompted me to think, or think again, about how I go about my own work in narrative. Approaching the literature in this way puts me in mind of Valery’s stance on reading the work of others: “But I am not much of a reader, since what I look for in a work is what will enable or impede an aspect of my own activity” (cited in Bayard, 2007, pp. 15–16). Yet for me, if not Valery, this is not to devalue the work of others, but to recognize that such works provide vital nourishment for my own. In what follows, I hope to encourage others to explore how they, too, might find nourishment in these pages.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.101 | 0.035 |
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".