Gamification of Life: Playing Computer Games to Learn, Train, and Improve Cognitively
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
This paper describes various ways in which computer games may be used throughout life to achieve goals such as improved reaction time, reduced memory loss, or improved understanding of subject-related concepts. It also describes project conducted in our research lab, where we work on finding ways to measure and potentially improve children’s cognitive processing (e.g., visual, auditory, and conceptual) through playing computer games. Our goals are to find the kind of cognitive effects, both major and minor, that specific computer games in our repository may have on children; find ways to evaluate a child’s performance during play, taking into account the child’s demographics, the gaming scores achieved, and time spent playing; relate the characteristics of the games and the child’s performance in play to possible strengths and weaknesses in the child’s cognitive processing; and to recommend remediation, in terms of the types of games that may be useful for the child to play next. Present state of our work is described, together with our short term and long term plans. DOI: 10.5901/jesr.2013.v3n8p83
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 | 0.001 | 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