“Martian Boneyards”: Sustained Scientific Inquiry in a Social Digital Game
Bibliographic record
Abstract
Social digital gaming is an explosive phenomenon where youth and adults are engaged in inquiry for the sake of fun. The complexity of learning evidenced in social digital games is attracting the attention of educators. Martian Boneyards is a proof-of-concept game designed to study how a community of voluntary gamers can be enticed to engage in sustained, high-quality scientific inquiry. Science educators and game designers worked together to create an educational game with the polish and intrigue of a professional-level game, striving to attract a new audience to scientific inquiry. Martian Boneyards took place in the high-definition, massively multiplayer online environment, Blue Mars, where players spent an average of 30 hours in the game over the 4-month implementation period, with some exceeding 200 hours. Most of the players’ time was spent in scientific inquiry activities and about 30% of the players’ in-game interactions were in the analysis and theory-building phases of inquiry. Female players conducted most of the inquiry, in particular analysis and theory building. The quality of scientific inquiry processes, which included extensive information gathering by players, and the resulting content were judged to be very good by a team of independent scientists. This research suggests that a compelling storyline, a highly aesthetic environment, and the emergent social bonds among players and between players and the characters played by designers were all responsible for sustaining high quality inquiry among gamers in this free-choice experience. The gaming environment developed for Martian Boneyards is seen as an evolving ecosystem with interactions among design, players’ activity, and players’ progress.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".