Bibliographic record
Abstract
Reading fiction is an important information behavior, but systematic study in our field about fiction has been sparse. This paper is part of continuing research about how fiction is informative. It reviews work about the ontological status of literary characters and how they can affect and inform us, especially in creating and contesting social boundaries, based in part on a small empirical study (n=8) of adult readers’ reading as adolescents. Such work helps us to understand important elements of people’s information behavior too often ignored. La lecture de la fiction est un comportement imformationnel important, mais les études systématiques portant sur la fiction sont rares dans notre domaine. Cet article fait partie d'un projet recherche sur l'informativité de la fiction. Il passe en revue les travaux sur le statut ontologique des personnages littéraires et la façon dont ils peuvent nous affecter et nous informer, en particulier dans la création et la contestation des frontières sociales, en partie sur la base d’une petite étude empirique (n = 8) sur la lecture des lecteurs adultes à l’adolescence. Un tel travail nous aide à comprendre des éléments importants du comportement informationnel trop souvent ignorés.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".