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Record W2605589709 · doi:10.3968/9194

Information Construction of News Discourse Under the Perspective of Intertextuality

2017· article· en· W2605589709 on OpenAlexvenueno aff
Jiansheng Yan

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsIntertextualityPolysemyLinguisticsNarrativeCohesion (chemistry)SociologyInterpretation (philosophy)NoveltyMetaphorLiteratureComputer sciencePsychologyArtPhilosophySocial psychology

Abstract

fetched live from OpenAlex

Intertextuality refers to the relationship of mutual penetration among texts, no text stands alone but is interlinked with the tradition that came before it and the context in which it is produced. As a genre of media report that conveys information, news discourse is characterized by its documentary, novelty, timeliness, and universality. Some news discourses reveal penetrability with others, causing intertextuality and providing a wider space to interpret them. Meanwhile, intertextuality also makes the narrative space optimization and discourse information complex or polysemy. Therefore, an accurate understanding of a piece of news depends on not only the coherence within a single text, but mutual interpretation among similar discourses. The distribution of news discourse information abides by tree structure, sometimes news understanding is related to human’s cognitive activities.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0040.013
Scholarly communication0.0120.019
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.028
GPT teacher head0.405
Teacher spread0.378 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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