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Record W4385321043 · doi:10.21134/mhjournal.v14i.2049

Algoritmos, IA y automatización en comunicación. Prólogo

2023· article· es· W4385321043 on OpenAlexaff
Raquel Victoria Benítez Rojas

Bibliographic record

VenueMiguel Hernández Communication Journal · 2023
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsHumanitiesArtPhilosophyPolitical science

Abstract

fetched live from OpenAlex

When we talk about Artificial Intelligence, AI for short, we can evoke futuristic or robotic scenes, where a series of algorithms created by human beings have advanced so far that they have taken power, making those who created them become slaves to them. Paradoxes aside, what is clear is that artificial intelligence has been among the inhabitants of planet earth since long before we can imagine. We must go back to 1940, when the British Alan Turing decoded messages from the enemy front during the Second World War with his Enigma Machine. Since then, this medium has evolved a lot.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.008

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 head0.040
GPT teacher head0.276
Teacher spread0.236 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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