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Record W2883287261 · doi:10.36829/63cts.v5i1.618

Diez pasos básicos para escribir y publicar un artículo científico

2018· article· es· W2883287261 on OpenAlexaff
Gerardo Arroyo, Armando Cáceres

Bibliographic record

VenueCiencia Tecnologí­a y Salud · 2018
Typearticle
Languagees
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

La elaboración de un manuscrito y su publicación en revistas indexadas, es la culminación de un proyecto de investigación, es la forma en que los investigadores comparten el nuevo conocimiento con sus pares y constituye el principal mecanismo de visibilidad personal e institucional, a nivel nacional e internacional. El presente ensayo tuvo como objetivo elaborar una guía básica de cómo preparar un manuscrito y brindar elementos prácticos para lograr su publicación. Se describen los 10 pasos, que señalan el orden en que deben abordarse las secciónes de un artículo científico, así como el contenido de las mismas. Finalmente se presenta una figura que mediante preguntas e indicaciones, resume el contenido de cada una de las secciones del artículo. Se espera que con esta guía, los investigadores encuentren una herramienta útil que les ayude a escribir y publicar artículos científicos.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.008
Science and technology studies0.0060.010
Scholarly communication0.0250.014
Open science0.0020.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0180.007

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 head0.043
GPT teacher head0.259
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations2
Published2018
Admission routes1
Has abstractyes

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