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Record W4416716850 · doi:10.11144/javeriana.uh94.dmoi

Destellos de «Mi Ojo Izquierdo» y Dibujos que Escuchan

2025· article· W4416716850 on OpenAlexaff
Alonso Gamarra, Marco Antonio Ramón Huaroto

Bibliographic record

VenueUniversitas Humanística · 2025
Typearticle
Language
FieldPsychology
TopicMemory, violence, and history
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWitnessGazeActive listeningState (computer science)Economic JusticeExpert witness

Abstract

fetched live from OpenAlex

Abstract: This essay explores the experience of Peruvian protographer Marco “Atoq” Ramón afeter partially losing his sight when he was shot by police covering a protest in the Puente Piedra disctrict of Lima in 2017. Through a dialogue between photography, drawing, and collaborative ethnography, the authors investigate the gaze that has emerged from this experience of state violence and impunity. Collaboratively, the text weaves together two threads: Atoq’s life and physical trajectory after the attack, and the concept of the “flash”, with which Atoq names the moments when one way of seeing fleetingly replaces another. In dialogue with anthropological debates on trauma, subjectivity, and the expressiveness of silence, as well as with experiences of eye mutilation in protest contexts in Chile and Colombia, this essay proposes drawing as a listening technique capable of recording gestures, affections, and exorbitant knowledge that exceed forensic and conceptual categries. Rather than interpreting violence, this practice seeks to bear witness to its interruptions, uphold ethical sensibilities, and foster emerging perspectives on life, death, and justice in a country ravaged by recurrent dispossession. Keywords: Photography, ethnography, drawing, violence, trauma, subjectivity, memory, social protest, justice.

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)0.003
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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