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Record W4385582939 · doi:10.1111/sena.12393

The politics of street names: Reconstructing Iran’s collective identity

2023· article· en· W4385582939 on OpenAlexaff
Ehsan Kashfi

Bibliographic record

VenueStudies in Ethnicity and Nationalism · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCollective memoryPoliticsForgettingIdentity (music)NarrativeHistorySociologyCollective identityState (computer science)Politics of memoryAestheticsLawMedia studiesLiteraturePolitical scienceArtLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract With the radical political change in 1979, Iran's revolutionary state assumed the responsibility of re‐rewriting the past history to forge a new sense of belonging, a particularly collective religious (Shia) identity. It launched a complex process of forgetting and remembering to first eliminate the national (Persian), non‐religious memories and heritage, associated and celebrated by the previous regime and then establish a sense of continuity with the country's Shia past; a feeling markedly engendered with a distinguishing symbolic reservoir of Shia traditions and memories, presented in history books, literature, the media, and everyday culture.This paper seeks to examine the role of street names in this process of reconstructing a new religious (Shia) collective memory and identity with particular reference to Tehran, Iran, during the 1979‐2019 period. It seeks to analyze changes in the city's street names and analyze the widespread renaming of streets and public spaces in the city as one means of both ‘de‐commemorating’ the pre‐revolutionary regime and marking the Shia legacy and memories as the signifiers of a widespread political maneuver to articulate a new version of the past and narrative of identity since the 1979 revolution.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
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 head0.116
GPT teacher head0.399
Teacher spread0.284 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

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