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Record W3201602799 · doi:10.24875/ric.21000287

My Years as Coeditor of the Revista de Investigación Clínica and the 1993-1998 Readers of Our Journal

2021· article· es· W3201602799 on OpenAlexaboutno aff
Alvar Lorı́a

Bibliographic record

VenueRevista de investigaci�n Cl�nica · 2021
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Library scienceHistoryComputer science

Abstract

fetched live from OpenAlex

Some information on our readers during my years as coeditor of the Revista de Investigación Clínica are presented on the basis that readers, unlike editors, have no conflict of interest in evaluating scientific publications.The information provided is restricted to the past 6 years (1993-1998) of my 11 years as coeditor since the number of reader visits to our on-line articles began in 1993.The nearly 500 articles published in the 6-year lapse were visited by more than 1 million readers with an outstanding 1998 year of almost 100 articles and near a quarter of a million visits.These figures suggest that our journal became at the end of the 20 th Century a valuable information source for physicians in Mexico.It was the original idea that led Salvador Zubirán to launch this journal in 1948.

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)0.017
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.005
Science and technology studies0.0030.003
Scholarly communication0.0160.006
Open science0.0020.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0400.016

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 head0.056
GPT teacher head0.413
Teacher spread0.357 · 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 designObservational
DomainEvaluation
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
Published2021
Admission routes1
Has abstractyes

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