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
Creating video games is a lengthy and demanding process. Financial success for games studios often depends on making games that deliver a fun and engaging experience for a diverse audience of players. Therefore, understanding how players interact and behave during gameplay is of vital importance. Playtesting aims to assist developers to achieve their design intent and help to identify and resolve potential problem areas during development. However, playtests are not always feasible or affordable for smaller, independent game developers (indie studios) because they require specialized equipment and expertise. In addition to this, there is a lack of research on the value of playtesting for indie studios, which means most indie developers are not convinced of the value of user research and playtesting. This paper reports on our collaboration with six commercial indie developers conducting eleven rounds of playtesting session. Through these collaborations, our paper contributes to this growing domain by highlighting the value of playtesting for indie developers and discussing the user research process and approaches based on indie developers' needs and budget.
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 | 0.000 |
| 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