MétaCan
Menu
Back to cohort
Record W4402512749 · doi:10.26485/ps/2024/73.3/7

Show that we exist: Iraqi women’s stories, Rita Leistner, and her seeking human(ity) in the images of conflict

2024· article· en· W4402512749 on OpenAlexaboutno aff

Bibliographic record

VenuePrzegląd Socjologiczny · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyGender studiesPsychologySocial psychology

Abstract

fetched live from OpenAlex

This article presents an analysis of one of the socio-artistic projects of the Canadian photographer Rita Leistner. Her work focuses mainly on how humans function during conflict and crises, including internal ones, but also includes initiatives related to environmental protection and fortitude displayed by her characters in everyday life. By focusing on the human condition and the situation of humanism today, Leistner demonstrates concern and a profound sense of empathy. Our reflections focus on the Safer here project, which tells the story of women incarcerated against their will in a Baghdad psychiatric hospital. Leistner presented their daily struggle for survival, their dignified lives, and how they come to terms with their plight. It was originally part of Unembedded, a project of four photojournalists stationed in Iraq. Our aim is to place Leistner’s story in a broader context that considers how war stories are created and presented to the world. We also consider the demanding and difficult role played by artists in this process, who try to find a balance between being interpreters and being historians. Leistner’s work shows her as, above all, an activist fighting for public attention and empathy for war victims. We use storytelling and visual analysis in our empirical investigations.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.024
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.092
GPT teacher head0.339
Teacher spread0.246 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePrzegląd SocjologicznySame topicMiddle East and Rwanda ConflictsFrench-language works237,207