The art in fiction: From indirect communication to changes of the self.
Bibliographic record
Abstract
Abstraction and thevividness of details in fiction . Paper presented at the 111th annualconvention of the American Psychological Association, Toronto, Can-ada.Mar, R. A., Oatley, K., Hirsh, J., dela Paz, J., & Peterson, J. B. (2006).Bookworms versus nerds: Exposure to fiction versus non-fiction, diver-gent associations with social ability, and the simulation of fictionalsocial worlds. Journal of Research in Personality, 40, 694–712. doi:10.1016/j.jrp.2005.08.002Mar, R. A., Oatley, K., & Peterson, J. B. (2009). Exploring the linkbetween reading fiction and empathy: Ruling out individual differencesand examining outcomes. Communications: The European Journal ofCommunication, 34, 407–428.Mar, R. A., Tackett, J. L., & Moore, C. (2010). Exposure to media andtheory-of-mind development in preschoolers. Cognitive Development,25, 69–78. doi:10.1016/j.cogdev.2009.11.002McCrae, R. R., & Costa, P. T., Jr. (1990). Personality in adulthood . NewYork, NY: Guilford Press.McCrae, R. R., & Costa, P. T., Jr. (1996). Toward a new generation ofpersonality theories: Theoretical context for the five-factor model. InJ. S. Wiggins (Ed.),
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".