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Record W4223637997 · doi:10.1177/00961442221080404

Who Killed Granada? From “the Beautiful” to “the Wounded” City of Falla and Lorca

2022· article· en· W4223637997 on OpenAlexaboutno aff
Francisco J. Giménez-Rodríguez

Bibliographic record

VenueJournal of Urban History · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Literature and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryArtDreamRomanceMythologyPunkMusicalQuarter (Canadian coin)SingingArt historyPaintingHumanitiesVisual artsLiteratureHistoryArchaeology

Abstract

fetched live from OpenAlex

Two opposed conceptualizations of Granada, shared across poetry, painting (and photography) and music, combined to create a contrasting transmedia soundscape of the city throughout the twentieth century. Granada “the Beautiful” responded to the romantic myth of the city and was rooted mainly in the Alhambra, an Arab palace among the mountains, filled with gardens and water fountains. This idealized image could be heard in Manuel de Falla’s literary and pictorial dream: Noches en los Jardines de España [Nights in the Garden of Spain] (1916), and also appeared in Federico García Lorca’s musical conception of his city (1933). Fifty years later, the democratic transformation of Spain generally led to a punk, poetic deconstruction of the city in Rimado de ciudad [Rhyming from the City] (1983), singing about the margins of the city: Granada “the Wounded”, emerging from the suburbia and from the dark streets of the Arab quarter: the Albaicín.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.020
Scholarly communication0.0120.004
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.179
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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