O fenômeno gacha pelo framework MDA: a game art colecionável e o game design
Bibliographic record
Abstract
Este artigo parte de uma pesquisa mais ampla e busca discutir, a partir do conceito de Mecânica, Dinâmica e Estética proposto por LeBlanc (2004), o fenômeno do sistema gacha em jogos digitais. Objetiva-se compreendê-lo de forma mais conceitual, desde sua origem a partir de máquinas de venda de colecionáveis, e de forma mais prática, por meio da análise de casos de games populares (Fate/Grand Order, SuperStar e Genshin Impact). Busca-se, assim, entender o envolvimento da game art como elemento de game design e compreender como o sistema é utilizado de maneira a engajar o jogador. Apresenta-se aqui o conceito de ludificação e intervenção baseada em jogos, para então ponderar sobre a viabilidade do uso do gacha em sistemas gamificados. São discutidos, dessa maneira, pontos relevantes para arte e design nos video games, que podem contribuir para a utilização do gacha em intervenções baseadas em jogos, recomendando-se assim o aprofundamento do estudo em etapas futuras da pesquisa.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".