The keys to success: Supplemental measures of player expertise in Massively Multiplayer Online Games
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
Abstract In this article we describe an investigation of player expertise deployed as part of a mixed-methods longitudinal, multi-site study that examined whether and how players’ offline characteristics are recognizable in their online interactions in Massively Multiplayer Online Games (MMOGs). After detailing our methodology and analytical toolkit, we narrow our focus to a case study that examines three players with previous experience in First-Person Shooter (FPS) games playing Rift (Trion Worlds 2011) (a fantasy-themed MMOG) for the very first time. This case study illuminates how interpretation of data can be inadvertently influenced by the researcher’s choice of technologies and methods employed in their study design. In particular, we demonstrate that initial research assessments of a player’s level of skill may be inaccurate and how the use of multiple data sources acts as a means for triangulating observations and analyses providing a richer – yet more complicated – view of player expertise.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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