Navigating Class, Gender, and Urban Mobile Spaces: Dissecting Iranian Car Social Spaces in Cinematic Narratives
Bibliographic record
Abstract
This study scrutinizes the active role of mobile urban spaces in shaping and generating social space. It explores the depiction of car spaces in two Iranian films in their cinematic narratives, symbolic meanings, and influence on the perceptions of urban mobile space, often referred to as third spaces in the urban studies literature. This interdisciplinary paper investigates the socio-cultural manifestations of the car interiors in two hybrid docufiction films: Ten, directed by Abbas Kiarostami, and Taxi, by Jafar Panahi. Built on the new mobilities paradigm’s perspective on the mobile space of cars wherein social space is inevitably produced and re-produced, this paper reveals the socio-cultural dynamics of the car space in the films’ representations. The car space produces subjectivities, exhibits socio-cultural foundations, offers a sense of belonging and place-making, and provides opportunities for informal social interactions, while embodying power dynamics. The central aim is to revise our conceptualizations of mobility spaces by examining spatial practices that revolve around the car spaces. The paper integrates cinematic representation as a resource for planners and social scientists to conceptualize mobility spaces, introducing diegetic cabinography filmmaking style.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".