Playing ‘for Real’: A Lab-Based Study of MMOGs
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
In this paper we report on a 3-year, mixed-methods study of Massively Multiplayer Online games, focusing on the ways in our lab-based studies were indeed sites of ‘real’ play, notwithstanding their limited ecological validity (Williams, 2010). We document the ways in which we observed players’ real commitment to a play session that had few or no opportunities for follow up – investing considerable time and attention to, for example, naming and customizing their avatars, and selectively equipping them. We illustrate here some of the insights available through lab-based play that cannot be captured otherwise. We also draw attention to the ways in which relying on only one type of data can create a false and/or incomplete picture of a participant’s level of engagement with the game. This research suggests that labs might well be a site where ‘authentic’ play is indeed possible, and can therefore offer rich potential for MMOG research as they can give significantly greater context than is possible from data that is generated by game servers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it