A strategy for reducing skills gap for the game development sector of the Indonesian creative industries
Bibliographic record
Abstract
The Indonesian creative economy has been on the rise since 2015 when it has started being measured and prioritized by the government. Its contribution towards the Indonesian GDP has risen significantly as well. A small part of that creative sector is the video game industry and market. The video game global market will be worth USD 200 Billion in 2023. Indonesia currently is ranked 16th in terms of market size. Although having an enormous market opportunity, local video game producers only contribute 1%. Growth opportunities exist, however local game studios are facing the difficulty of recruiting quality game developers. Higher education institutions need to produce graduates having the knowledge, competences, and skills relevant for their work. This study is done to identify and prioritize attributes for the design of a university level program in game development that ensures employability in the sector. A qualitative thematic analysis is done in identifying the important factors for an academic program, followed by an analytical hierarchical process in determining the factors. Result of the study shows that a curriculum with internships in game studios, ensuring students are knowledgeable on the business models & video game market, having practitioners teach in the program, and creating a community of practice in the university is essential in producing quality graduates.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".