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Record W4392419815 · doi:10.7202/1109101ar

INTELIGENCIA ARTIFICIAL Y MEDIACIÓN

2024· article· es· W4392419815 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLex Electronica · 2024
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicLaw, Ethics, and AI Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComputer science

Abstract

fetched live from OpenAlex

La intención de este trabajo, consiste en exponer las problemáticas que derivan del uso de las nuevas tecnologías en el campo de la mediación, con la finalidad de generar debates sobre las condiciones de su utilización. La ponencia se divide en dos partes, en la primera, pasamos revista sobre las notas características, ventajas y desventajas respecto de estas tecnologías, examinando, además, los desafíos que plantea su implementación, mientras que, en la segunda; nos adentramos en el análisis de algunos puntos críticos en cuanto a su utilización en el ámbito de la mediación. En atención a la amplitud que implica el abordaje de temáticas que se vinculan con las distintas herramientas tecnológicas, hemos puesto el foco en la inteligencia artificial, en la creencia de que amerita un tratamiento urgente, dadas las consecuencias que de su uso pueden derivar.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.009

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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