Emergent Ambient Culture in Smart Cities: Exploring the Internet of Cultural Things (IoCT) and Applications in 21st Century Urban Spaces
Bibliographic record
Abstract
This work explores the Internet of Cultural Things (IoCT) and applications in the context of smart cities and learning cities. Ambient culture is advanced as an emergent form of the IoCT and applications in the context of 21stcentury everyday interactions in urban spaces. The constructs of awareness, learning, openness, and engagement enable formulation and operationalization of a framework for ambient culture in support of this study of the Internet of Cultural Things (IoCT) and applications in smart cities. A case study approach is used in the gathering of qualitative and quantitative data through interviews and surveys conducted with diverse individuals across several cities in Canada and Europe. In parallel with this study, anecdotal evidence is gathered from individuals and groups about smart cities, enabling further analysis and triangulation of data. This work contributes to the research literature across several domains including the IoCT and applications in relation to smart cities and learning cities. Future research and practice directions are identified for ambient culture with implications for ambient heritage, libraries, data relationships, and data infrastructures going forward.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".