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Record W2766280004 · doi:10.1386/jgvw.9.2.123_1

The culture of permadeath: Roguelikes and Terror Management Theory

2017· article· en· W2766280004 on OpenAlexaff
Rob Parker

Bibliographic record

VenueJournal of Gaming & Virtual Worlds · 2017
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTerror management theoryFunction (biology)SociologyPsychological resilienceFocus (optics)Resilience (materials science)PsychologyManagement theoryLens (geology)Social psychologyAestheticsArtEngineeringManagement scienceOptics

Abstract

fetched live from OpenAlex

Abstract In this exploratory article, Rob Parker examines the function of permadeath in the roguelike subgenre of computer role-playing games (cRPGs) through the lens of Terror Management Theory (TMT). In doing so, he provides a broad history of the subgenre, with specific attention to providing a productive distinction between modern and traditional roguelikes that lies in how they handle permadeath. He suggests that this focus on permadeath is one of the primary motivating factors for the resilience of the roguelike community over time, and what the implications of those motivations might mean for other members of the gaming community.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.028
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.321
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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