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Record W4415478060 · doi:10.1162/leon.a.2556

Artful Minds, Healing, and Well-Being: Women, Art, Science, and Technology in Latin America, 1970s to the Present

2025· article· en· W4415478060 on OpenAlexaff
Claudia Costa Pederson, Gabriela Aceves, Pat Badani

Bibliographic record

VenueLeonardo · 2025
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLatin AmericansCitizen journalismSelection (genetic algorithm)Latin American studiesFocus (optics)

Abstract

fetched live from OpenAlex

Abstract Conceived as part of a broader recovery of histories of Latin American women working with science and technology, “Artful Minds, Healing and Well-Being: Women, Art, Science, and Technology in Latin America 1970s to the Present” includes a selection of projects that share Leonardo’s focus on topics related to health. Ten artists from five Latin American countries (Mexico, Brazil, Colombia, Argentina, and Chile) represent a sampling of relevant practitioners. The works span fifty years, from the 1970s to the present, and involve various fields and technologies, from sonic, video, computational, and AI arts, to medical technologies and bioart, to technologically aided installations and performances. Created in various formats as solo-authored, collaborative, and participatory works, these projects include diverse physical and virtual environments, from artistic, domestic, scientific, and electronic spaces to urban and rural areas in Latin America and Europe.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.009
Scholarly communication0.0050.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.341
Teacher spread0.326 · 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
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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