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
How do we read stories? How do they engage our minds and create meaning? Are they a mental construct, a linguistic one or a cultural one? What is the difference between real stories and fictional ones? This book addresses such questions by describing the conceptual and linguistic underpinnings of narrative interpretation. Barbara Dancygier discusses literary texts as linguistic artifacts, describing the processes which drive the emergence of literary meaning. If a text means something to someone, she argues, there have to be linguistic phenomena that make it possible. Drawing on blending theory and construction grammar, the book focuses its linguistic lens on the concepts of the narrator and the story, and defines narrative viewpoint in a new way. The examples come from a wide spectrum of texts, primarily novels and drama, by authors such as William Shakespeare, Margaret Atwood, Philip Roth, Dave Eggers, Jan Potocki and Mikhail Bulgakov.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it