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Record W7066019094

Games People Play: Exploring DePaul's Top-Rated Computer Game Development Program

2014· article· en· W7066019094 on OpenAlexaboutno aff

Bibliographic record

VenueThe Institutional Repository at DePaul University (DePaul University) · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsVideo game developmentGame DeveloperGame programmingVideo game designGame design documentGame designGame development toolConjunction (astronomy)Game art design
DOInot available

Abstract

fetched live from OpenAlex

In March 2014, the Princeton Review, in conjunction with PC Gamer magazine, named the top 25 schools to study game design in the United States and Canada. DePaul's undergraduate program ranked 20th, a considerable leap from 2013’s honorable mention. The graduate program came in at 12th. DePaul's strong ranking reflects the game development program's extension of its basic game development, software engineering and programming to include art, design and storytelling, as well as computer graphics technology, networking, artificial intelligence and human-computer interaction. Examples of the award-winning games developed by students and now marketed by such going concerns as Sony PlayStation are described.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.082
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0040.002
Scholarly communication0.0150.005
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.014
GPT teacher head0.181
Teacher spread0.167 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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