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“Don’t tell us we’re not Cuban!” How political nostalgia makes Miami and Miami makes nostalgia political

2024· article· en· W6929255233 on OpenAlexaff

Bibliographic record

VenueVU Research Portal · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsMiamiPoliticsHomelandFraming (construction)Narrative

Abstract

fetched live from OpenAlex

This chapter explores how cultural memory, projected onto Miami from both sides of the Florida Straits, converges to create the imagined place of Cuban heritage in Miami that to a large degree serves as the foundation for political activism in US state- and national politics. From the Freedom Tower, where arriving Cubans from 1962 to 1974 were given assistance, to the cafés where Cuban-Americans get their “cafecitos,” Miami is awash in Cuban cultural memory. (Levine & Asís, 2000; Grenier & Moebius, 2015) The early history of the Cuban community is, however, all but non-existent in the narratives of the diaspora, which focus on the post-1959 waves of migration in response to the government of Fidel Castro. The chapter will consider the earliest campaigns to promote Miami as a city, in the 1890s, focused on entrepreneurial Cuban cigar factories, giving streets and places Cuban names and even importing building materials from Havanese houses (Sicius, 1998). Similarly, the chapter addresses the Cuban government's framing of Miami as a place outside Cuban history and finally, it highlights the subjective narratives of Cuban migrants in Miami, and their nostalgic effort to frame Miami as a Cuban place, while simultaneously maintaining ownership of the homeland of Cuba. This chapter is based on a methodology that addresses the full range of cultural memory from its canonisation (A. Assmann, 2008), through its mediation (Erll, 2011) to its reception into a subjective imagination. The latter step will be based on the analysis of a series of oral history interviews, conducted in Miami Dade County with Cuban-Americans who arrived between the Mariel Exodus of 1980 and the Balsero Crisis of 1994. These will be analysed through a method based on the work of Gunnar Olsson and his concept of cartographic reasoning (Olsson, 2007).

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.116
GPT teacher head0.337
Teacher spread0.221 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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