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
To determine the effects of gamification on student education, researchers implemented "Kaizen," a software-based knowledge competition, among a first-year class of undergraduate nursing students. Multiple-choice questions were released weekly or biweekly during two rounds of play. Participation was voluntary, and students could play the game using any Web-enabled device. Analyses of data generated from the game included (1) descriptive, (2) logistic regression modeling of factors associated with user attrition, (3) generalized linear mixed model for retention of knowledge, and (4) analysis of variance of final examination performance by play styles. Researchers found a statistically significant increase in the odds of a correct response (odds ratio, 1.8; 95% confidence interval, 1.0-3.4) for a round 1 question repeated in round 2, suggesting retention of knowledge. They also found statistically significant differences in final examination performance among different play styles.To maximize the benefits of gamification, researchers must use the resulting data both to power educational analytics and to inform nurse educators how to enhance student engagement, knowledge retention, and academic performance.
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 | 0.001 |
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