Architecture of Monumentality: A Critical Analysis of In-Game Representations of Washington, D.C.
Bibliographic record
Abstract
In recent decades, video games have become a billion-dollar industry, serving as a ubiquitous interactive media. With significant technological advancements in graphics and performance, their fidelity in the representation of the built environment has improved the immersive experience, leading to increased impact on the player’s understanding of the architectural identity of places. Video games reference urban conditions to contextualise narratives yet are able to provide an additional dimension of navigability and interactivity within these virtual spaces. As a result, this medium has a considerable influence on the players’ awareness of and engagement with architectural and urban environments. The digital representation of spatial conditions becomes a substantial component of level design, with titles often relying on tropes and iconography associated with particular cities to contextualise virtual environments. Due to globalisation, Washington D.C. has become universally recognised as the epicentre of Western politics, housing many iconic architectures that shape their distinctive urban environments. Numerous video games take advantage of this recognizability and significance by siting their narratives within the city’s iconic urban and cultural identity. This paper examines the potential influence of in-game caricatures of architectural space on identity and urban memory within the global collective consciousness. By analysing the portrayal of Washington D.C. within prevalent video games; the paper examines their approach and use of urban context within the gameplay. Engaging several game modalities, including alternative histories, post-apocalyptic futures, espionage, and vehicular exploration, this paper presents trends of representation predominant within individual game categories. Through the assessment of the aforementioned titles, abstraction of the urbanscape, celebrity of architectural landmarks, contextualisation through prominent architectures, and ironic manipulation of architectural imagery emerge methods of falsification of the urban context. This paper concludes with a discussion of the impact of in-game representations of cities on the public’s perception and understanding of architectural and urban space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".