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Record W2962711625 · doi:10.1558/jca.36745

Inspecting the Foundation of <i>Mystery House</i>

2019· article· en· W2962711625 on OpenAlex
John Aycock, Katie Biittner

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.

Bibliographic record

VenueJournal of Contemporary Archaeology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsMacEwan UniversityUniversity of Calgary
Fundersnot available
KeywordsAdventureArtifact (error)ProgrammerComputer scienceCode (set theory)Field (mathematics)Game designGame development toolVideo game developmentGame art designVisual artsHuman–computer interactionProgramming languageArtificial intelligenceArtSet (abstract data type)

Abstract

fetched live from OpenAlex

Computer games are recent artifacts that have had, and continue to have, enormous cultural impact. In this interdisciplinary collaboration between computer science and archaeology, we closely examine one such artifact: the 1980 Apple II game Mystery House, the first graphical adventure. We focus on implementation rather than gameplay, treating the game as a digital artifact. What can we learn about the game and its development process through reverse engineering and analysis of the code, data, and game image? Our exploration includes a technical critique of the code, examining the heretofore uncritical legacy of Ken Williams as a programmer. As game development is a human activity, we place it in a theoretical framework from archaeology, to show how a field used to analyze physical artifacts might adapt to shed new light on digital games. Open Access Attribution-NonCommercial-NoDerivatives: CC BY-NC-ND

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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