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Record W2624194797 · doi:10.1177/1461444817711403

Megabooth: The cultural intermediation of indie games

2017· article· en· W2624194797 on OpenAlexafffund
Felan Parker, Jennifer R. Whitson, Bart Simon

Bibliographic record

VenueNew Media & Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of WaterlooConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndie filmLegitimacyIntermediationIndependence (probability theory)Value (mathematics)Field (mathematics)IntermediaryBusinessSet (abstract data type)Political scienceSociologyPublic relationsMarketingMedia studiesComputer scienceLaw

Abstract

fetched live from OpenAlex

This article considers the history, practices and impact of the Indie Megabooth and its founders in terms of their role as a 'cultural intermediary' in promoting and supporting independent or 'indie' game development. The Megabooth is a crucial broker, gatekeeper and orchestrator of not only perceptions of and markets for indie games but also the socio-material possibility of indie game making itself. In its highly publicized outward-facing role, the Megabooth ascribes legitimacy and value to specific games and developers, but its behind-the-scenes logistical and brokerage activities are of equal if not greater importance. The Megabooth mediates between a diverse set of actors and stakeholders with multiple (often conflicting) needs and goals and in doing so helps constitute the field of production, distribution, reception and consumption for indie games. 'Indie-ness' and independence are actively performed in and through intermediaries such as the Megabooth.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.008
Scholarly communication0.0080.006
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.043
GPT teacher head0.331
Teacher spread0.287 · 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

Citations91
Published2017
Admission routes2
Has abstractyes

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