Griefing and Deception in Video Games: Examining Attitudes towards the Phenomena
Bibliographic record
Abstract
Though not new to online gamers, griefing – an act of play intended to cause grief to game players – is understudied in LIS scholarship. We expand on the definition of griefing for library contexts by considering its deceptive elements and examining gamers’ attitudes in a gaming forum and an e-mail survey.Bien que connu des joueurs de jeux vidéo, le terme griefing, qui fait référence à un acte dont le but est de gêner ou d’emmerder les autres joueurs, est sous-étudié dans le domaine de la bibliothéconomie et des sciences de l’information. Nous avons étendu la définition de griefing au contexte bibliothéconomique en considérant sa nature trompeuse et en examinant les attitudes des joueurs d’un forum de jeu et les réponses d’un questionnaire par courriel.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".