Trains of thought: narrative foreshadowing and predictive processing in <i>Anna Karenina</i>
Bibliographic record
Abstract
This article shows how the cognitive theory known as “predictive processing” can expand our understanding of the ways in which readers are primed by textual cues in Lev Tolstoi’s Anna Karenina to predict story developments, and how this process is linked to narrative momentum and affective engagement. In turn, this study expands the applicability of predictive processing, which relies on the idea that the human brain routinely predicts and updates information in unfolding scenarios, to literary contexts, in productive combination with narrative theories of foreshadowing and schema usage. The authors examine how these systems of cues motivate and shape involvement in the fictional scenarios of Anna Karenina and how they contribute to the text’s foreboding narrative draw – most notably with regard to the redolent motif of suicide by train.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".