Bibliographic record
Abstract
There is nothing so strange it cannot be true, and no story so unlikely it cannot be told. No story is a lie, for a tale is a bridge that leads to the truth. ( The Arabian Nights , retold by Neil Philip) This book is about how the centuries-old thoughts quoted above are indeed true. Why are stories not lies, even though they don’t tell the truth? How do they help us to learn from our experience and the experience of others? And how does language support the meaning of stories? The structure of that “bridge . . . to the truth” is what I will try to understand. The human ability, or even desire, to tell, understand, watch, and create stories has engaged a number of disciplines, each of which poses a different set of questions about the core of the phenomenon. Why do we enjoy stories? What’s in it for us as a species? Could our culture exist without stories? Are they a mental construct, a linguistic construct, or a cultural construct? Is there a difference between real stories and fictional stories? These are just some of the questions of interest to anthropologists, psychologists, narratologists, philosophers, linguists, and literary scholars. The answers have been, by necessity, partial, and directed at the interests of the disciplines they emerge from, but it is becoming increasingly clear to all concerned that some cross-disciplinary dialogue is necessary. This book is an attempt to bring together at least some of the questions out there while focusing on one central aspect of storytelling: how do stories construct meaning?
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".