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Record W3127913854 · doi:10.1177/1750698020988759

Online memorials as a platform for empathy journalism

2021· article· en· W3127913854 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMemory Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité du Québec à Montréal
KeywordsEmpathyJournalismHonorPortraitNarrativeNewspaperMedia studiesPublishingSociologyHistoryPsychologyArtArt historyLiteratureInternet privacySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Shortly after the suicide bombings and mass shootings that took place in and around Paris on 13 November 2015, journalists of the French daily newspaper Le Monde decided to honor and commemorate the victims by publishing their portraits and creating an online memorial called #EnMemoire (#InRemembrance). Until now, studies of these types of memorials have concentrated primarily on analyses of portraits and their narratives. They have not, however, focused on the environments in which they were produced and received. Likewise, no study has yet explored the journalist’s role or the place of empathy in the online-memorial creation process. Based on memory and journalism studies, this article discusses therefore the online memorial creation process and the role empathy plays in the ways journalists—as mediators of mourning—and readers interact with each other. It also addresses sensitivities the study researcher developed after experiencing these events.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.406
Teacher spread0.269 · 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