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Record W3127913854 · doi:10.1177/1750698020988759

Online memorials as a platform for empathy journalism

2021· article· en· W3127913854 on OpenAlexafffund
Katharina Niemeyer

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.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.006
Scholarly communication0.0080.006
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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

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

Citations3
Published2021
Admission routes2
Has abstractyes

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