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Producción científica en Scopus de los institutos de salud especializados públicos de Perú, 2010-2022

2023· article· en· W6910162705 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueRevista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo · 2023
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsScopusNova scotiaContext (archaeology)Latin Americans

Abstract

fetched live from OpenAlex

Objetivo: Evaluar la producción científica de los institutos de salud de Perú en Scopus, 2010-2022. Métodos: Estudio bibliométrico realizado en Scopus durante septiembre del 2022, en 14 institutos de salud especializados públicos de Perú. Incluimos estudios originales que tuvieran al menos un autor de alguno de los institutos. Resultados: Los institutos incluidos publicaron entre 0 y 347 artículos originales (H-index entre 0 y 51). Los institutos de la ciudad de Lima fueron los que tuvieron mayor producción. En los siete institutos con mayor producción, el porcentaje de artículos con autor corresponsal del instituto evaluado varió entre 22.3% y 36.7%, y el porcentaje de estudios que declararon ser financiados por el instituto varió entre 0% y 11.6%. Conclusión: La producción científica de los institutos evaluados fue heterogénea, a predominio de aquellos ubicados en Lima. Los institutos raramente participaron en el financiamiento de los estudios publicados.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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