A secure digital asset managment network for game development and education
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
The lack of freely available high-quality game development assets is an issue that affects instructors of courses in game development. When purchasing commercial assets for use in independent games, restrictive license agreements must be agreed to which prohibit the re-distribution of the model data and typically limiting the number of uses to a single project or developer --- licensing the models on a per student basis can become prohibitively expensive. Re-distribution of the asset data is however possible as long as the data is compiled into the executable providing a security through obscurity copy protection scheme. This paper focuses on developing a secure method for distribution of digital assets suitable for educational models of development, i.e. classroom or lab settings. We propose the development of a Secure Digital Rights Management Network that enables flexibility in the use of secured assets while maintaining security and limiting their re-distribution. The application of such a system is to be incorporated into an educational setting where students can use the secure assets during the development of class projects while preventing the re-distribution of the copyrighted data.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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