Trends of a Digital Art Competition in the Early 21st Century : A 24-Year History of the Asia Digital Art Award FUKUOKA
Bibliographic record
Abstract
While digital art competitions have updated their award fields according to rapid technological developments, these updates have not been examined by the existing literature. This study investigates the activity and achievement of the Asia Digital Art Award FUKUOKA (ADAA), which has been held annually since 2001 as an international competition to reveal the characteristics and development of digital art culture in the first quarter of the twenty-first century. Data from 24 annual competitions (2001-2024) were analyzed, including archived websites, catalogs, and classified submission records. Updates to the sections and the categories as well as the changes in the composition of judges over time were evaluated to examine how the award was revised. The number of submissions and awards was examined to assess ADAA’s influence. ADAA started with three sections – non-interactive art, interactive art, and digital design. Non-interactive art was divided into still and moving images in 2004. The digital design section was replaced with entertainment (applied industry) in 2008. The definitions of the sections were updated in the first five years and between 2012-2013. The total number of submitted works was 17,136, with 2,221 awards given to creators from 25 countries and regions. Between 2017-2024, the number and ratio of submissions for the entertainment (applied industry) section were higher in the student category than in the general category (p < 0.01). By adapting to technological changes, ADAA has remained a relevant and influential competition for digital art creators who have the potential to innovate art culture and the content industry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".