Bibliographic record
Abstract
One of the prominent features of literary reading is a sense of defamiliarization: a passage describing an object, event, or person in the mundane world unexpectedly seems strange, so that the reader is made to pause or slow the pace of reading in order to reflect. In Owen Barfield’s words, such moments seem to come from “a different plane or mode of consciousness” (Poetic diction: a study in meaning. McGraw-Hill, New York, 1964, p. 171), and they demonstrate the “unfamiliar” of the artwork discussed by Shklovsky (Art as technique. In: Russian formalist criticism: four essays, eds. and trans. Lemon LT, Reis MJ. University of Nebraska Press, Lincoln, 1965, p. 12). I identify several mental processes that help constitute the sense of strangeness and that may contribute distinctive elements to the presence of literariness. I examine the initial moments of the experience of literary reading, those occurring in the first few hundred milliseconds as suggested by studies of EEG waves: these include absence of habituation, the deferral of intention, the thwarting of prototypical feeling, bodily alertness, and the experience of animacy. I then consider some sequential features that guide and shape response on a larger scale, focusing in particular on the processes of feeling and their impact on the reader.
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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".