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Record W2412796783 · doi:10.1386/jaws.2.1.43_1

Arcade fever: Economics, affect and interface design of the 1970s and 1980s video arcade

2016· article· en· W2412796783 on OpenAlexaff
T. Pullen

Bibliographic record

VenueJAWS Journal of Arts Writing by Students · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsPopularityAffect (linguistics)Video gameReading (process)Interface (matter)Computer scienceGame designMultimediaHuman–computer interactionPsychologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

Abstract How is it that the paradigm shift from mechanical to video-based arcade gaming has created a loyal video game following that holds true today? This article will explore the historical development of video arcade games, their instantaneous popularity and their continued presence in the world of contemporary gaming. In reading the affective experience of gaming through the lens of actor-network-theory (ANT), a number of factors can be specified for the popularity of nostalgic gaming: the affective design of interface, the emergent symbols of the arcade game, the spectacular nature of play, the mastery of a game (maximizing play per dollar spent), and the affective potential of precognitive actions and its relation to nostalgia. This leads to the conclusion that all these factors co-produce a wholly novel experience particular to the network of the arcade.

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: none
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0050.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.023
GPT teacher head0.313
Teacher spread0.289 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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