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
Unlike recreational games, serious games do more than entertain the player. Serious games promote acquisition of information and skills that are valued in both the virtual world and the real world. The challenge is to design and develop serious games that simultaneously create an enjoyable experience for the player as the player develops or improves her skill set as a result of game play and applies these newly developed skills in a real world setting. Because transfer of learning represents the primary goal of serious games, it is crucial that game designers understand the interactions associated with game tasks and their impact on players prior to game development. Borrowing heavily from interaction design, we introduce the user centered game design methodology as the framework for serious game design and apply this technique to the evaluation of the social interactions between Player Characters in a commercial Massive Multiplayer Online Role Playing Game. Significant results from experimental studies suggest that this genre of games shows great promise as an unorthodox language learning tool for vocabulary acquisition and reveals the importance of social interactions in the virtual space of video games. Finally, we discuss the design implications for serious games that facilitate Second Language Acquisition.
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) | 0.001 | 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