Bibliographic record
Abstract
Digital twins are characterized as NextGen smart cities, part of cyber-manufacturing processes and as industry 4.0 systems. They are digital replicas of ‘real’ world physical assets, with real-time information interchanges via sensors between digital replicas and their material assets. They can be immersive 3D environments of cities rendered in game engines and engaged with via VR headsets. Uncritical, enthusiastic and technologically solutionist rationales are generally offered for their creation in IT, engineering and vendor literature. Digital twins are often touted as a system to predict, preempt, prevent and plan for the impact of climate change and for emergency preparedness scenario planning. This paper discusses preliminary observations from a transdisciplinary archival and AI research project of an urban digital twin in the architecture, engineering, construction and owner operated (AECOO) sector, theoretically framed by critical data studies, archival digital diplomatics, digital records forensics and social and technological assemblage theory. A hybrid methodological approach combines a technological walkthrough with twin developers mapping data flows and technological processes, guided by a semi-structured interview instrument to identify what constitutes a record of automated and AI/ML actions that affect social and material outcomes. Preliminary observations identified a complex set of technopolitics, myriad procurement and contractual arrangements where data and technology ownership remains with vendors. It has become clear that digital twins are very messy and poorly governed infrastructures of infrastructure, which are mostly proprietary, lack interoperability and standards. The aim is to understand urban digital twins so that they may be governed in the public interest.
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 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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".