Serious indie games for social awareness
Bibliographic record
Abstract
By avoiding holistic and accurate portrayals of the physical limitations of human beings, contemporary games ultimately fail to acknowledge the more marginalized qualities of humankind in their characters. For example, we often see idealized human heroes who require no food, water or sleep in games from all generations, such as the majority of characters in first person shooters (FPS), role-playing games (RPG) and action adventure games. Such characters shrug off extreme bodily damage and are almost always in perfect mental health. 3=3 aims to expand the definition of what it means to experience the journey of an ideal protagonist through the realization and development of three characters, each of whom have an individualized disability or impairment. Inspired by games with proven design frameworks, game mechanics and user experiences, their journey together to survive a contemporary disaster scenario becomes a memorable experience of emotive and relational understanding. 3=3 aims to encourage a positive shift in the way gamers understand and perceive the embodied experiences that manifest from the addressed disability demographics, not only in virtual characters, but in real life encounters. Additionally, the success of 3=3 would invoke new perceptions in regards to how notable characters can be portrayed, encouraging game designers to consider more realistic, inclusive portrayals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".