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Record W2755445730 · doi:10.17118/11143/11235

«Come stiamo a lingua? … Risponde il linguista». La divulgazione del sapere linguistico nelle cronache linguistiche fra gli anni 1950 e il Duemila

2017· article· it· W2755445730 on OpenAlexvenueno aff
Sabine Schwarze

Bibliographic record

VenueCircula · 2017
Typearticle
Languageit
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesLingua francaArtPhilosophy

Abstract

fetched live from OpenAlex

This paper deals with the rhetoric of articles about language related topics published in Italian newspapers in a period of substantial changes in the field of the standard norm and the language use in the high and public discourse spheres. The sample of texts which serves as an empirical basis comes from language columns which provide critical, informative or instructive comments on the "correct or adequate" use of the Italian language, texts which were signed by specialists in the field of literature, philology and linguistics. We compare two language columns published in two of the most renowned national daily newspapers, La Stampa and La Repubblica, between the 1950s and the first decade of the 21st century. The aim of this comparison is to identify rhetorical strategies adopted by the authors to examine both the reflection on scientific paradigms which have been the subject of linguistic research on the popular discourse on language and the relevance of specific discourse traditions for the selection of these strategies.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.007
Scholarly communication0.0060.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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.

Study designQualitative
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

Citations5
Published2017
Admission routes1
Has abstractyes

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