Visual Representations of Happiness in Portuguese Adolescents
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".