Bibliographic record
Abstract
Fiction might be dismissed as observations that lack reliability and validity, but this would be a misunderstanding. Works of fiction are simulations that run on minds. They were the first kinds of simulation. All art has a metaphorical quality: a painting can be both pigments on canvas and a person. In literary art, this quality extends to readers who can be both themselves and, by empathetic processes within a simulation, also literary characters. On the basis of this hypothesis, it was found that the more fiction people read the better were their skills of empathy and theory-of-mind; the inference from several studies is that reading fiction improves social skills. In functional magnetic resonance imaging meta-analyses, brain areas concerned with understanding narrative stories were found to overlap with those concerned with theory-of-mind. In an orthogonal effect, reading artistic literature was found to enable people to change their personality by small increments, not by a writer's persuasion, but in their own way. This effect was due to artistic merit of a text, irrespective of whether it was fiction or non-fiction. An empirically based conception of literary art might be carefully constructed verbal material that enables self-directed personal change. WIREs Cogn Sci 2012, 3:425-430. doi: 10.1002/wcs.1185 For further resources related to this article, please visit the WIREs website.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".