Deception in video games: examining varieties of griefing
Bibliographic record
Abstract
Purpose Though not new to online gamers, griefing – an act of play intended to cause grief to game players – is fairly understudied in LIS scholarship. The purpose of this paper is to expand the inventory of griefing varieties, consider their deceptive elements and examine attitudes towards the phenomenon. Design/methodology/approach The authors collected and content analysed 80 (non‐elicited) posts from the Something Awful forum thread and compared them to the results of ten (elicited) e‐mail interviews. Findings As a complex phenomenon, griefing has multiple interpretations and opposing attitudes. The thread results show that griefers, as perpetrators, have predominantly positive or neutral attitudes towards the act. About 15 per cent of the examined griefers reportedly resort to deceptive techniques. More extravagant griefs that require verbal interactions in player‐versus‐player (PvP) online games involve deception and often fall into two categories: scamming or greed play (prioritising personal benefits). The authors found self‐reported instances of deception by scheming, luring, entrapment, pretence and verbal concealment in griefing acts. The interview respondents, as predominantly victims of griefs, do not think of griefing (or may not be aware of it) as an act of deception and primarily associate it with harassment (inciting emotional reactions) or power imposition (exerting superiority). Casual griefing – refusing to comply with the rules for mere entertainment – stands out as another griefing variety. Originality/value With the growth of popularity of video gaming, libraries are largely unaware of griefing and should be prepared to address it in video game use policies for online gaming units or tournaments. Online gaming affords a unique opportunity to examine deception in computer‐mediated human‐to‐human communication. The complexity of the phenomenon and associated opposing views are offered here to be weighted by the LIS scholars and professionals.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".