MétaCan
Menu
Back to cohort
Record W3168598750 · doi:10.7203/rd.v1i7.184

Uso del clickbait en los medios nativos digitales españoles. Un análisis de El Confidencial, El Español, Eldiario.es y Ok Diario

2021· article· es· W3168598750 on OpenAlex

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

VenueRevista Dígitos · 2021
Typearticle
Languagees
FieldSocial Sciences
TopicCommunication and COVID-19 Impact
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

El clickbait es una estrategia utilizada en los medios digitales, que busca llamar la atención a través de los titulares, apelando a las emociones y a la curiosidad de los lectores para que cliquen en la noticia. Para indagar en este fenómeno, se procede a estudiar a través de una metodología cualitativa y cuantitativa los titulares de las portadas de los cuatro principales diarios nativos digitales en España durante una semana (n=2505): El Confidencial, El Español, eldiario.es y Ok Diario. Entre los resultados de la investigación destaca una presencia alta de titulares que contienen clickbait (48% en el cómputo global, ascendiendo al 69,5% en las soft news), siendo las modificaciones de morfosintaxis de las oraciones el recurso más utilizado. Se discuten estos hallazgos en el marco de las dinámicas de la comunicación digital, cuestionando si el clickbait, como fenómeno extendido en los diarios nativos digitales, puede llevar a un mayor número de visualizaciones en sus páginas, pero en detrimento de la calidad de las informaciones publicadas.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.361
Teacher spread0.339 · 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