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Record W2326739093 · doi:10.1177/1750698010392959

Defacing memory: (Un)tying Peru’s memory knots

2011· article· en· W2326739093 on OpenAlexaff
Cynthia E. Milton

Bibliographic record

VenueMemory Studies · 2011
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTyingCollective memoryNarrativeMeaning (existential)SociologySpace (punctuation)Closure (psychology)Historical memoryHistoryAestheticsMedia studiesLawPolitical sciencePsychologyLiteratureArtComputer science

Abstract

fetched live from OpenAlex

This article parses opposing currents in Peru’s collective memory of their bloody internal war (1980—2000) through an analysis of acts of vandalism perpetrated against one of the country’s few sites of memory, the Ojo que llora, in Lima. ‘Vandalism’ in this article is understood as a form of writing (though a violent one) of an alternative vision of the past. Originally intended as a space for remembering and paying homage to the victims of the armed conflict, the site has become a space for contesting disputed memories. As a site of performance of memory and human rights claims, and especially as the target of continued defacement, the Ojo que llora has become a stage on which the perduring presence of the past — in its still-conflictual strains — is made visible for national and international publics. It thus refuses the very closure that government narratives would impose, and thereby keeps open public engagement with the past. The ongoing conflicts over the past made visible at this site point to the struggles to define an over-arching memory, and in the process the very meaning of ‘victim’ is constrained.

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)0.002
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.025
Scholarly communication0.0100.015
Open science0.0010.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.148
GPT teacher head0.341
Teacher spread0.193 · 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".

Quick stats

Citations18
Published2011
Admission routes1
Has abstractyes

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