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Record W4411277117 · doi:10.3390/adolescents5020026

Visual Representations of Happiness in Portuguese Adolescents

2025· article· en· W4411277117 on OpenAlexaff
Teresa Freire, Andreia Ramos, Beatriz Raposo, Jenna Hartel

Bibliographic record

VenueAdolescents · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPortugueseHappinessPsychologyCognitive psychologySocial psychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Background/Objectives: Happiness is a main topic of psychological research, and as a catalyst for transformative change, it is capable of inspiring growth and well-being. This study aims to identify and understand the themes that compose visual representations of happiness in adolescents, while using an innovative qualitative methodology centered on visual research. Methods: Applying the ‘draw-and-write’ technique, Portuguese adolescents were asked to ‘Draw happiness’, generating a visual data set of 330 drawings, coined hSquares. Results: By order of prevalence, the thematic analysis identified eight key themes: ‘people’, ‘hobbies’, ‘love’, ‘smile’, ‘sports’, ‘basic needs’, ‘inner harmony’, and ‘human rights and equality’. The findings highlight the significance of social contexts, such as family and peer relationships, as central to adolescents’ happiness, while also emphasizing the importance of self-selected activities. Visual representations associated with basic needs and human rights emerged as novel contributions to the literature. Differences emerged by age, with younger adolescents often depicting single themes, whereas older adolescents integrated multiple themes in their drawings. Conclusions: This study provides a visual complement to the rich textual conversation about happiness and demonstrates the potential of visual methodologies in psychological research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.373
Teacher spread0.356 · 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 designObservational
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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