Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it