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Record W26118266 · doi:10.3138/cjfs.20.2.19

Making Sense of Early Video Arcades: The Case of Pittsburgh, 1980-1983

2011· article· fr· W26118266 on OpenAlexvenueno aff
Ryan Pierson

Bibliographic record

VenueCanadian Journal of Film Studies · 2011
Typearticle
Languagefr
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Au début des années 1980, les bornes d’arcade payantes furent des sites locaux d’exposition au matériel audio-visuel non seulement dans les arcades mais aussi dans les restaurants, les laveries automatiques et d’autres espaces publics. Cet article veut contribuer à la jeune histoire des médias numériques en considérant les arcades de jeu vidéo comme un lieu de contact aux nouveaux médias. Les arcades de jeu sont cruciales pour la généalogie des nouveaux médias parce que leur interface graphique a constamment influencé celui des ordinateurs personnels et a eu une « existence publique » spécifique. Utilisant la ville de Pittsburg comme milieu d’étude, cet article documente les manifestation d’anxiété qui se sont produite à une échelle locale en rapport avec les arcades de jeux vidéos et les consoles de jeux, et aussi les règlements visant à contrôler leur usage. En dépit d’un discours de panique entourant ces technologies et de plusieurs tentatives de règlementation, les bornes d’arcade, parce qu’elles se sont avérées finalement peu dérangeantes, ont résolument conservées leur place dans l’espace public.

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)0.008
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 score0.515
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0200.011
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.133
GPT teacher head0.329
Teacher spread0.196 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Film StudiesSame topicDigital Games and MediaFrench-language works237,207