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Record W4416405636 · doi:10.36151/rcdi.2025.806.06

Derecho al honor versus libertad de expresión en redes sociales. el comportamiento beligerante y agresivo de las comunicaciones en redes sociales

2025· article· W4416405636 on OpenAlexaff
Elena Fernández de la Iglesia

Bibliographic record

VenueRevista crítica de derecho inmobiliario/Revista crítica de derecho inmobiliario · 2025
Typearticle
Language
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsHonor

Abstract

fetched live from OpenAlex

En términos generales, el discurso de la agresividad verbal engloba todas aquellas expresiones consideradas peligrosas para la estabilidad de los sistemas democráticos. Entre ellas se encuentran las ofensas basadas en expresiones machistas frente a criterios feministas. La protección jurídica de estas expresiones continúa generando interesantes discusiones en diversos tribunales constitucionales y supranacionales. Nuestro artículo pretende explicar algunos de los principales criterios jurídicos que el TEDH y siguiendole nuestros tribunales (Supremo y Constitucional) que han adoptado sobre este tema en favor de la libertad de expresión. En el caso objeto de comentario dos son las cuestiones importantes a examinar, por un lado, la ideología del personaje que puede ser apreciada como violenta hacia las mujeres por su posicionamiento ideológico, donde prevalece el derecho a la libertad de expresión sobre el derecho al honor del sujeto, y por otro lado, la responsabilidad del titular de la cuenta de Facebook que permite los comentarios de sus seguidores, descalificadores, insultantes y amenazantes, no pudiendo de desentenderse sin más de lo que se publica en su perfil por otros usuarios.

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 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)0.015
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.006
Science and technology studies0.0050.004
Scholarly communication0.0090.002
Open science0.0090.004
Research integrity0.0050.008
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 head0.030
GPT teacher head0.324
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueRevista crítica de derecho inmobiliario/Revista crítica de derecho inmobiliarioSame topicHate Speech and Cyberbullying DetectionFrench-language works237,207