Feeling through Images: Architectural Histories after the Emotional Turn
Bibliographic record
Abstract
This set of Field Notes explores the emerging topic of urban emotions by foregrounding the critical role of images in mediating how built environments are experienced, interpreted, and imagined. While social histories have increasingly engaged with the ‘emotional turn,’ the spatial and visual implications of this shift remain underexplored. We argue that both still and moving images – whether artistic, journalistic, documentary, or otherwise – operate as powerful modes of representation that not only depict urban realities but also actively construct them. Their rhetorical and affective force offers a critical lens for understanding the entanglements between emotions and the built environment. Drawing insights from the 2024 workshop of the EAHN’s Urban Representations Interest Group, we outline diverse case studies that reveal how emotions such as anticipation, anxiety, outrage, longing, and detachment circulate through images and shape urban imaginaries. These contributions argue that emotional responses mediated through visual culture are integral to interpreting the built environment. By situating images at the centre of scholarly enquiries, we call for a broader methodological engagement with emotions within architectural and urban scholarship. The emotional life of images, we contend, not only enriches historical and critical analysis but also illuminates the dynamic processes through which cities are produced and reproduced.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".