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Record W2887646305 · doi:10.1075/jial.00009.ell

Harnessing the roar of the crowd

2018· article· en· W2887646305 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Internationalization and Localization · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsMultinational corporationPreferenceVideo gameRelation (database)Social mediaData collectionPsychologyAdvertisingPublic relationsSociologyComputer sciencePolitical scienceBusinessMultimediaWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

Abstract Through quantitative data analysis, this study explores the attitudes of gamers from different French-speaking locales (Belgium, France, Canada, and Switzerland) in relation to their language preference and opinions of translated material while playing video games. The intended goal is to develop a replicable methodology for data collection about the linguistic preferences of video game players. The research strategy is based on online questionnaires distributed to gamers through social media. The results highlight players’ level of satisfaction regarding the localisation of games and suggest that industry strategies put forward till recently may be rather inadequate. Linguistic preferences seem to vary within locales based on factors such as English language proficiency and personal background. The results of this research may serve the implementation of new localisation strategies for video game products in French-speaking countries of emerging markets or other multinational languages.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.262
Teacher spread0.233 · 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