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Record W2460170930 · doi:10.1177/1555412016653035

Virtual Total Control: Escaping a Simulated Prison

2016· article· en· W2460170930 on OpenAlexaff
Kristine Levan, Steven M. Downing

Bibliographic record

VenueGames and Culture · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPrisonControl (management)Construct (python library)PsychologyInstitutionalisationSociologySocial psychologyCriminologyComputer science

Abstract

fetched live from OpenAlex

Previous studies have examined media portrayals of total control and institutionalization in prison, and a few studies have considered the connection between media portrayals and depictions of prison escape attempts. The current inquiry seeks to fill this gap in the literature through an autoethnographic case study of the video game The Escapists, in which players assume the role of an inmate whose ultimate goal is to escape prison amid an environment populated by other nonplayer character inmates and guards. In this inquiry, specific attention is paid to the player’s experiences as a subject of control from guards, inmates, surveillance systems, and the prison construct, and how these interactions contextualize and potentially motivate the player to attempt escape. Connections between virtual and real-world escape attempts are discussed. Conceptual and theoretical links between total control and interactive experiences of simulated prison life, as well as implications of this study, are examined.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.006
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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 designNot applicable
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

Citations3
Published2016
Admission routes1
Has abstractyes

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