Groundhog DAG: Representing Semantic Repetition in Literary Narratives
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses the concept of semantic repetition in literary texts, that is, the recurrence of elements of meaning, possibly in the absence of repeated formal elements. A typology of semantic repetition is presented, as well as a framework for analysis based on the use of threaded Directed Acyclic Graphs. This model is applied to the script for the movie Groundhog Day. It is shown first that semantic repetition presents a number of traits not found in the case of the repetition of formal elements (letters, words, etc.). Consideration of the threaded DAG also brings to light several classes of semantic repetition, between individual nodes of a DAG, between subDAGs within a larger DAG, and between structures of sub-DAGs, both within and across texts. The model presented here provides a basis for the detailed study of additional literary texts at the semantic level and illustrates the tractability of the formalism used for analysis of texts of some considerable length and complexity. 1
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it