Photogenic Urban Landscapes: Towards an Intermedial Framework for Landscape Criticism in the Age of Social Media
Bibliographic record
Abstract
Drawing on cultural geography, visual cultural studies and theories of media ecology, this essay lays out a framework for collaboration between media scholars, architects and critics, positioning photographic social media such as Instagram as a means for pursuing questions about the changing role of landscape in the visual mediation of urban social life and public culture. While buildings and urban infrastructure also have mediating functions, I focus on designed landscapes such as public parks because they mediate visual perception in a manner that is historically intertwined with that of photography. Responding to existing interest in the critical reading of landscape values as well as research on new photographic forms and practices arising from social media use, I suggest that we take seriously the idea that landscape is itself a form of media. Attending to its ongoing interactions with other media will enable us both to specify the nature of its intermediality in a given time and place, and open a new space for reflexivity and critique. Beginning with an account of what is at stake in the visual mediation accomplished through urban landscapes on the one hand, and in the study of social media use on the other, I make a case for a critical, qualitative analysis of photographic content as opposed to quantitative analytics of the data associated with it. I then present an example of the kind of analysis I have in mind, drawing from an exploratory case study of photographs from Grand Park in Los Angeles (Rios Clementi Hale). In the process, I flesh out the concept of intermediality as it pertains to designed landscapes and demonstrate the kinds of questions and pedagogical opportunities such an approach may open.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.014 | 0.101 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.008 |
| 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".