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Record W1838496027 · doi:10.4000/sdj.521

Technologie et design de jeu

2015· article· fr· W1838496027 on OpenAlexaff
Jonathan Lessard

Bibliographic record

VenueSciences du jeu · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La technologie est au cœur du discours sur l’évolution des jeux ; des jeux vidéo, en premier lieu, mais également de nombreux sports. Les études du jeu actuelles ne rendent pourtant pas bien compte des interactions entre design de jeu et technologie. Cette dernière est généralement réduite aux notions de média ou de plateforme, qui impliquent des artefacts standardisés médiatisant des structures de jeux abstraites. Un tel point de vue ne rend compte ni de l’influence rétroactive du design de jeu sur la technologie, ni de la matérialité du processus de design, ni de la mobilisation de la technologie par les joueurs. Dans cet article nous proposons un modèle d’une plus fine granularité, mettant en lumière les rapports dynamiques entre design de jeu, technologie et le jeu (play) des joueurs. Ce modèle est fondé sur une méta-analyse historique de l’évolution de plusieurs jeux numériques et non-numériques.

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 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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.161
GPT teacher head0.358
Teacher spread0.197 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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