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Record W3019966350 · doi:10.5070/c391042320

Il visuale italiano nella crisi della cittadinanza. L’Italianness nei dispositivi di cattura neoliberali del “Migrant Cinema”

2020· article· it· W3019966350 on OpenAlexfundno aff
Eleonora Meo

Bibliographic record

VenueCalifornia Italian Studies · 2020
Typearticle
Languageit
FieldSocial Sciences
TopicItalian Fascism and Post-war Society
Canadian institutionsnot available
FundersPrinceton UniversityUniversity of TorontoPurdue University
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

L'articolo, adottando un approccio transdisciplinare e intersezionale vicino ai cultural studies , alla visual culture e ai critical migration studies , tenta di ricostruire lo spazio di articolazione italiano della crisi discorsiva dello spazio europeo , analizzando in che modo le produzioni visuali italiane esprimono l'attuale crisi discorsiva della cittadinanza. All’interno del panorama visuale italiano dedicato al tema, alcune produzioni visuali finiscono per ricadere in un sistema di cattura neoliberale di rappresentazione della cittadinanza che qui viene definito ‘Migrollywood’, un sistema che invisibilizza la vita delle nuove generazioni di italiani ( New Italians of color ) all'interno della rappresentazione della migrazione. Attraverso la lettura contrappuntistica di due produzioni visuali del campo artistico-cinematografico: il documentario 18 Ius Soli (2011) del regista italo-ghanese Fred K. Kuwornu e il lungometraggio Per un figlio (2017) del regista italo-srilankese Suranga D. Katugampala, l'articolo indaga come viene rappresentata l’identità italiana ( Italianness) e in che modo il visuale è in grado di costituire un metodo per una contro-epistemologia di decolonizzazione della cittadinanza e dei suoi confini interni, dislocando il punto di vista privilegiato – di razza, genere, classe, religione, ecc. – del cittadino italiano.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.003
Science and technology studies0.0040.003
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.006

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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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