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Record W4295074070 · doi:10.11144/javeriana.uh90.mmfc

Una metodología para modelar fenómenos culturales latentes: interés público en la red Razón Pública

2022· article· es· W4295074070 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

VenueUniversitas Humanística · 2022
Typearticle
Languagees
FieldSocial Sciences
TopicComputational and Text Analysis Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Este artculo ampla el horizonte de anlisis en el campo de las redes culturales, al proponer una metodologa basada en datos para modelar fenmenos culturales latentes. A modo de ilustracin, se aplic el diseo metodolgico en un estudio sobre los temas de inters pblico en la revista Razn Pblica. Primero, se revis el trmino inters pblico y se justic la pertinencia de concebir este tipo de temas como fenmenos culturales latentes en la red de la publicacin. Segundo, se present el periodismo digital en Colombia como el dominio cultural de la red. Para la elaboracin del modelo, se propuso una estrategia para delimitar, obtener y estructurar los nodos de los actores y objetos culturales. Finalmente, se explic el uso de la tcnica Asignacin Latente de Dirichlet (LDA) para modelar los temas de la revista y se valid con una encuesta que conrm los temas de inters pblico, segn algunos actores de la red.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.049
GPT teacher head0.353
Teacher spread0.303 · 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