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Record W2252227653

Groundhog DAG: Representing Semantic Repetition in Literary Narratives

2013· article· en· W2252227653 on OpenAlex
Greg Lessard, Michael Levison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLiterature, Language, and Rhetoric Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsRepetition (rhetorical device)Computer scienceFormalism (music)NarrativeNatural language processingLinguisticsArtificial intelligenceLiteraturePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

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

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.300
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it