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Record W2237411472 · doi:10.4185/rlcs-2015-1080

La experiencia de elaborar infografías didácticas sobre diversidad sexual

2015· article· es· W2237411472 on OpenAlexaff
Yunuén Ixchel Guzmán Cedillo, Diana Natalia Lima Villeda, Sirléia Ferreira da Silva Rosa

Bibliographic record

VenueRevista Latina de Comunicación Social · 2015
Typearticle
Languagees
FieldSocial Sciences
TopicAdvertising and Communication Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPsychologySociologyArt

Abstract

fetched live from OpenAlex

El objetivo de esta investigación es identificar los temas que se manifiestan en el discurso de 21 estudiantes de Medicina al escribir de forma individual acerca de su experiencia de elaboración de infografías didácticas sobre diversidad sexual. Se utilizó la técnica de tematización realizada por 2 jueces. Los resultados son tres temas relevantes: aprendizaje (51%), características de la infografía (38%) y propuestas (11%). El primero se manifiesta en tres vertientes: actitudinal, declarativo y procedimental; en el segundo los estudiantes caracterizan a la infografía como: atractiva, didáctica, de elaboración compleja e informativa; en el tercero la proponen como herramienta de aprendizaje. La elaboración de infografías didácticas se considera una estrategia para realizar investigación que responda dudas de quienes la elaboran, a la vez que buscan informar a otros sobre estos temas para promover el respeto a la diversidad sexual, contribuyendo así en la formación de ciudadanos y profesionales de la salud

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)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.065
GPT teacher head0.387
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2015
Admission routes1
Has abstractyes

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