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Record W4323344317 · doi:10.21066/carcl.libri.11.2.2

New Challenges in Children’s Illustration in Portugal

2022· article· en· W4323344317 on OpenAlexfundno aff
Carina Rodrigues

Bibliographic record

VenueLibri et Liberi · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLiteracy and Educational Practices
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversidade do MinhoInternational Council for Canadian Studies
KeywordsNarrativeReading (process)Articulation (sociology)Rhetorical questionComposition (language)PortugueseLinguisticsPoint (geometry)Process (computing)PoetryComputer scienceSociologyLiteratureArtPhilosophyPolitical science

Abstract

fetched live from OpenAlex

The aim of this paper is to reflect on some of the current trends in Portuguese illustration for children, focusing on an intertextual reading of the picturebooks written and illustrated by Manuela Bacelar, as a precursor in the creation of this kind of book in Portugal. Based on the discursive interdependence of text and illustrations, stylistic and technical-narrative procedures are observed, in necessary articulation with the visual/graphic and material/peritextual aspects. The potential of these aspects is highlighted in the composition of the work and in the construction of its multiple semiosis. The interdependence of text and illustrations poses specific requirements in the process of reading and has an impact on the formation of competent and autonomous readers. Relying on modern literary currents to be found in a postmodernist aesthetic, this paper examines the corpus, from both a technical and compositional point of view, seeking to investigate some of the traits and formal rhetorical, stylistic and thematic resources that distinguish the work of this awarded artist.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0080.013
Scholarly communication0.0120.004
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.071
GPT teacher head0.342
Teacher spread0.271 · 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 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
Published2022
Admission routes1
Has abstractyes

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