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
Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and spoils the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit it, to victims’ monetary losses due to cyber crime. This article studies cheaters in the Steam Community, an online social network built on top of the world’s dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also observed timing information of the cheater flags, as well as the dynamics of the cheaters’ social neighborhoods. We discovered that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. Moreover, we noticed that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. Also, we observed a social penalty involved with being labeled as a cheater: cheaters lose friends immediately after the cheating label is publicly applied. Most importantly, we observed that cheating behavior spreads through a social mechanism: the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. This allows us to propose ideas for limiting cheating contagion.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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