Profiling Academic Research on Massively Multiplayer On-line Role-Play Gaming (MMORPG) 2000-2009: Horizons for Educational Research
Bibliographic record
Abstract
Whilst there exists a large body of publications around Massively Multiplayer On-line Role-Play Gaming (MMORPG), there is little profiling academic research on this type of game. This study aims at unveiling what, when, where and who constitute scholarly work in research about MMORPG. A 777-register dataset was configured with primary documents taken from 16 databases and two web-portals. The dataset was drilled down using specialized text-mining software. Findings revealed four main research interests that comprise the games themselves, gaming experiences, systems architecture and educational MMORPG. It was also found that research on this topic started out in 2002 and some milestones of emerging research were charted out. The most prolific organizations and authors were also identified in which the USA, Canada and Italy occupy outstanding places. It is recommended that research profiling studies be carried out to extend more informed literature reviews and support further research questions.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.058 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".