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Record W4381462838 · doi:10.18778/1733-8077.19.2.03

Entering Iranian Homes: Privacy Borders and Hospitality in Iranian Movies

2023· article· en· W4381462838 on OpenAlexaff
Foroogh Mohammadi, Lisa‐Jo K. van den Scott

Bibliographic record

VenueQualitative Sociology Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHospitalitySociologyArchitectureMovie theaterPower (physics)Space (punctuation)TourismFocus (optics)Media studiesAdvertisingVisual artsComputer scienceArtPolitical scienceLawBusiness

Abstract

fetched live from OpenAlex

The architecture of homes in Iran has changed significantly over the past four decades since the 1979 Iranian revolution. We ask how these architectural changes shift neighborhood relationships and how they transform the Iranians’ hospitality rituals and practices. We conducted a qualitative content analysis of eighteen Iranian movies filmed after the 1979 revolution. They allowed us to make comparisons among various dwelling patterns and neighborhood relationships. We argue that the representations of neighborhood relationships reflect these changes, demonstrating the impact of architecture on interactions. Our focus in this article is on borders of privacy, power dynamics in the neighborhoods and among families, and communication forms to better understand the impact of changing architecture on hospitality through the lens of cinema. Additionally, we engage with Goffman’s (1956) concepts of frontstage and backstage, demonstrating that these are not dichotomous, although they are opposites, and there can be a thinning of frontstage along with a thickening of backstage. Entrances to homes are often gradual, and visitors may gradually penetrate through layers of the frontstage as they become closer (emotionally and in space) to the heart of the home’s (and its occupants’) backstage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.472
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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