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Record W1851258138 · doi:10.1080/1369118x.2012.756048

MAKING A NAME IN GAMES

2013· article· en· W1851258138 on OpenAlexaffabout
Alison Harvey, Stephanie Fisher

Bibliographic record

VenueInformation Communication & Society · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIncubatorStatus quoIndie filmValue (mathematics)Power (physics)EntrepreneurshipPublic relationsSociologyBusinessPolitical scienceComputer scienceMedia studies

Abstract

fetched live from OpenAlex

This article explores the development and implementation of a Toronto-based incubator supporting local women in developing their own games. The incubator was created to help change the current (male-dominated) status quo of game production, promising participants skills sharing, support for the development of a new game, and entry into the local community of indie games developers. It was at the same time part of a large network of commercial and non-commercial interests with a shared agenda of promoting the local digital innovation scene. These different motivations and actors are considered to understand the nature of this complex social network market and the circulation of particularly feminized affective labour therein, detailing how value, reward, and benefit are conceptualized throughout this network. The article focuses on how and where these understandings are in alignment and where they fall apart, revealing problematic structures of power and control linked in particular to gender and entrepreneurialism in the area of digital innovation.

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)0.007
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 score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.011
Scholarly communication0.0080.009
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.006

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 head0.030
GPT teacher head0.260
Teacher spread0.230 · 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

Citations30
Published2013
Admission routes2
Has abstractyes

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