Bibliographic record
Abstract
Introduction: Juiciness refers to the use of various audiovisual effects that are triggered in response to user interactions. This study explores the presence of juicy elements in interactive infographics, thereby extending the investigation of juiciness beyond traditional gaming contexts. Methodology: A descriptive research approach, employing content analysis, was used to evaluate a sample of interactive infographics published between 2010 and 2024. The sample was collected from four prominent sources, and each visualization was analyzed using a binary classification system (YES or NO) to indicate the presence of the identified juiciness elements: animation, particles, audio feedback, screen shake, and persistence. Results: Overall, the findings indicate that juicy elements are present in certain interactive visualizations, though not universally across all examples. The data revealed that animation appeared most frequently, with an occurrence rate of 73.85%, followed by particles (20.51%), audio feedback (5.64%), persistence (4.1%), and screen shake (1.03%). Furthermore, 25.64% of the visualizations contained no juicy elements. Discussion: The findings from the analysis reveal that juicy elements are present in a significant number of interactive visualizations, but none of the visualizations analyzed incorporated all five juicy elements simultaneously. Despite the presence of some juicy elements, no single visualization captured the full essence of juicy design, which ideally offers a high level of feedback from minimal user input. Conclusions: While no single visualization incorporated all five juicy elements, combinations of up to four were observed, suggesting that juiciness does not require a uniform or exhaustive application of all elements.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".