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Record W4232340216 · doi:10.7202/1086251ar

Rio de Janeiro as a Park of Ruins: Digging through Images in a Site-Specific Poetry Book

2021· article· fr· W4232340216 on OpenAlexvenueno aff
Diana Klinger, Vinícius Ximenes

Bibliographic record

VenueIntermédialités Histoire et théorie des arts des lettres et des techniques · 2021
Typearticle
Languagefr
FieldPsychology
TopicMemory, Trauma, and Testimony
Canadian institutionsnot available
Fundersnot available
KeywordsGarciaPoetryDiggingRelation (database)Reading (process)ArtOrder (exchange)Art historyHumanitiesAestheticsSociologyHistoryVisual artsArchaeologyLiteraturePhilosophyLinguisticsComputer science

Abstract

fetched live from OpenAlex

This text proposes a reading of Parque das ruínas (Ruins Park, 2018), a poetry book by Marília Garcia, which places words and images in a dialogue in order to question ways of seeing through constant changes of spatial and temporal scales. Mediated by optical devices, it exposes a body of perception as crossed by “practices, techniques, institutions, and procedures of subjectivation” (Crary, 2012). Our aim here is also a re-assembling of the book’s images in their relation to Rio de Janeiro’s history grounded in slavery and their related struggles regarding memory. By referring to the archaeological strata of Rio de Janeiro’s formation, moving through the “ruins” of its modern institutions such as the museum and the university discussed in Garcia’s book, we suggest an underlying sense that ties the poem to the present dynamics of extractive capitalism.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.342
Teacher spread0.296 · 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
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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