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Record W4413346843 · doi:10.1590/scielopreprints.13026

De Acceso Abierto a Ciencia Abierta: Lecciones de PKP en América Latina

2025· article· en· W4413346843 on OpenAlexaff
Alejandra Casas Niño de Rivera, Anne Clinio, Juan Pablo Alperín

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicScience, Technology, and Education in Latin America
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The Open Access movement has profoundly reshaped scholarly communication by promoting universal, free access to academic knowledge. In Latin America, this movement has evolved into a broader Open Science agenda, emphasizing not only access to publications but also the opening of data, methodologies, infrastructures, and governance models. This article examines the trajectory of the Public Knowledge Project (PKP) as a case study of how community-driven, open-source infrastructures can sustain the values of transparency, equity, and multilingual inclusion at a global scale. Drawing from nearly three decades of regional experience, we analyze PKP’s impact across UNESCO’s four pillars of Open Science: open knowledge, open infrastructures, dialogue with diverse knowledge systems, and societal engagement. Particular attention is given to the strategic role of Latin America, where decentralized, non-commercial models—now internationally recognized as Diamond Open Access—have long been the norm. We argue that PKP’s development illustrates both the opportunities and tensions of advancing Open Science: ensuring sustainability without commercial capture, balancing global standards with local needs, and safeguarding scientific knowledge as a public good. The lessons from Latin America highlight the importance of governance structures, inter-institutional collaboration, and epistemic diversity for building a more just, inclusive, and sustainable scientific ecosystem.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.380
Teacher spread0.368 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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
Published2025
Admission routes1
Has abstractyes

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