VISUAL SPACES OF CHANGE: UNVEILING THE PUBLICNESS OF URBAN SPACE THROUGH PHOTOGRAPHY AND IMAGE
Bibliographic record
Abstract
If we take for granted the idea of being in the so-called post-photographic era, the genre that can surely suffer its consequences and show its manifestations is that of architectural photography.Architectural photography no longer documents given that the veracity of what is represented is systematically under suspicion.It is assumed that what is photographed is, at best, an interpretation.Urban photography has lost the fascination it had at the time of the optimistic and utopian configuration of the modern city.Definitely overshadowed that enthusiasm, the exploration of the urban slides towards the transitional territories and the places in transit, where neither the city is a city nor the landscape a landscape.The non-places, the generic city or the third landscape, whatever we may name it, is where the scenery portrays a different ecosystem open to new conceptual and visual narratives that allow us to return to the consolidated city in order to discover something new, hidden behind its prejudices, their stereotypes and their history.In the bland is the substance.We could argue nowadays that what least interest architectural photography is architecture itself.The built object is not an end but a means: a visual resource to talk about its context and its sociological, political and cultural identity.The more critical and conceptual this approach is, the more artistic it becomes in practice.If the gaze is not innocuous and aseptic then it turns out to be truly personal: the author's questioning look prevails over the distant gaze of the pure depiction.That's where the emotion and the critical discourse arise.In the contradiction and chaos, the visual artist finds a new order to explain the notion of what was built and that city in transit, tired of its expansion at the rate of speculation and which seeks to withdraw and redefine itself from within, from its consolidated identity.Indeed, architecture photography, empowered by its leaning and artistic grounding, is more interesting when we understand it as a projective tool of an interdisciplinary nature.When the gaze at architecture and the urban is cross-sectional and polyhedral it is when it is certainly useful and effective.Photography becomes an instrument of research and analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".