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Record W7038724700

Inteligencia artificial generativa: Un contexto disruptivo en el acceso a la información [Generative artificial intelligence: A disruptive context for access to information]

2024· other· en· W7038724700 on OpenAlexfundno aff

Bibliographic record

VenueE-LIS Repository (University of Naples Federico II) · 2024
Typeother
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
FundersCollege of Pharmacy, University of MichiganNational Taiwan UniversityUniversiti Putra MalaysiaUniversity of TorontoMonash UniversityUniversity of New South WalesKarolinska InstitutetJohns Hopkins UniversityStanford Bio-XUniversity of MichiganHarvard University
KeywordsContext (archaeology)Relation (database)Generative grammarField (mathematics)Information technologyScientific field
DOInot available

Abstract

fetched live from OpenAlex

The year 2023 has seen an explosion of artificial intelligence tools that enable the generation of all kinds of content, which has had a profound impact at all levels and particularly in the field of education. These tools pose an unprecedented disruptive context in relation to access to information derived from their interactive, contextual and generative nature, whose implications probably go much further than other previous innovations and which require a reaction in the academic field, which must be based on clear policies and guidelines aligned with the principles of academic integrity and existing scientific evidence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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