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Record W3164682760 · doi:10.1177/09610006211016533

How are academic libraries in Spanish-speaking Latin America responding to new models of scholarly communication and predatory publishing?

2021· article· en· W3164682760 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Librarianship and Information Science · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPublishingLatin AmericansScholarly communicationPublic relationsLiteracyInformation literacyPolitical scienceSociologyLibrary sciencePedagogyComputer scienceLaw

Abstract

fetched live from OpenAlex

The topic of predatory publishing and ways to combat it is garnering considerable attention in many parts of the developed world, where academic librarians are emerging as leaders in this regard. However, less is known about how this phenomenon is playing out in developing regions, including Spanish-speaking Latin America. This study presents the results of a survey of 104 academic librarians in this region, along with follow-up interviews with seven respondents. The findings reveal that scholarly publishing literacy in general, and predatory publishing in particular, currently has low visibility in this part of the world, although there is growing recognition of and increasing concern about the issue. Although there is some debate about whether scholarly publishing literacy should be the sole responsibility of the library, many participants agree that the library has a role to play. Moreover, while most of the librarians who participated perceive that they have a solid knowledge of open access, they are less confident in their understanding of predatory practices and are seeking to increase their skills and knowledge in this regard to better support researchers at their institutions. To address this shortcoming, academic librarians in the region have expressed an interest in receiving training and in participating in international collaborations with other libraries that have already developed resources or programming in this area.

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)0.018
metaresearch head score (Gemma)0.082
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesBibliometrics, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.082
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0440.110
Science and technology studies0.0000.000
Scholarly communication0.0350.245
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.424
GPT teacher head0.455
Teacher spread0.031 · 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