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Record W2076288546 · doi:10.5539/ass.v8n14p49

A Cross-disciplinary Approach to Degree Programs in Video Games

2012· article· en· W2076288546 on OpenAlexvenueno aff
Michael Hitchens, Rowan Tulloch, Adam Ruch

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkComputer scienceProcess (computing)CurriculumDisciplineDegree programMultimediaSociologyPedagogyMedical education

Abstract

fetched live from OpenAlex

Macquarie University, in 2012, introduced two undergraduate coursework programs in the area of video games. These programs are a joint initiative of the Departments of Computing and Media, Music, Communication and Cultural Studies. The programs represent an innovative approach to curriculum structure in this area, combining technical, design and reflective critical practice to produce rounded graduates with a wide knowledge of issues and practices in interactive media. This paper describes the process of designing these programs, the aims and rationales guiding their design and their detailed structure. The central guiding principle behind the programs was that accomplished designers of interactive media, particularly video games, need both a sound technical background and an appreciation of the relationship between users, society and their designs. This is reflected in both the structure of the programs and the pedagogical approaches in the specialist units.

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)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0120.005
Scholarly communication0.0070.003
Open science0.0020.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.074
GPT teacher head0.339
Teacher spread0.265 · 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 designTheoretical or conceptual
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

Citations1
Published2012
Admission routes1
Has abstractyes

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