Bibliographic record
Abstract
In recent years, with the coming of age of a new generation of social activists raised on video games and the release of new tools that make video game development easier for individuals and small groups, many activists have begun to release video games designed to further their cause. However, there is little evidence as to the effectiveness of video games in changing attitudes. The video game, Homeless: It’s No Game was developed to determine whether people could be persuaded to become more sympathetic to the plight of the homeless by playing the role of a homeless woman in a video game and whether this persuasive effect could be measured. Volunteers were recruited to answer a survey of attitudes towards the homeless and were then assigned to either play the game, read a short story about homelessness, or to be part of a control group, after which the survey was re-administered. Results were mixed, with some indicators showing an increase in sympathy towards the homeless and others showing no significant effect. There were also some indications that playing the video game led to a strengthened belief in the effectiveness of video games in raising awareness of social issues. The results indicate that games can help reinforce a social activist message, especially if their audiences consider them realistic.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".