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Record W3013013892 · doi:10.5430/ijhe.v9n3p214

Publishing at Any Cost? The Need for the Improvement of the Quality of Scholarly Publications

2020· article· en· W3013013892 on OpenAlexvenueno aff
María José Sá, Carlos Miguel Ferreira, Ana Isabel Santos, Sandro Serpa

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingDynamismPromotion (chess)Quality (philosophy)Context (archaeology)Open access publishingPublic relationsProcess (computing)Element (criminal law)Scholarly communicationPolitical scienceLibrary scienceComputer scienceLawHistory

Abstract

fetched live from OpenAlex

At a time of great dynamism among publishers of scientific publications, with the inevitability of Open Access and the ease of publishing online at low cost, it is possible to find publications with different levels of scientific respectability. In this context, the improvement of the quality of scholarly publications emerges as a critical element for publishers, authors and academic institutions, as well as for society in general. This opinion piece discusses Open Access journals with different levels of quality, focusing on the following quality-promoting measures: blacklists, author’s preparation, and institutional prevention. The analysis allows concluding that the open review will be one of the key elements in the process of clarification and promotion of the level of quality and consequent scientific respectability of each of the Journals, of the thousands currently existing, a number that is likely to increase.

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)0.032
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.187
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0040.008
Scholarly communication0.0320.036
Open science0.0020.005
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0190.006

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 head0.618
GPT teacher head0.604
Teacher spread0.014 · 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
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

Citations7
Published2020
Admission routes1
Has abstractyes

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