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Record W7115817944

Post-Holocaust Dialogue as a Path to Reconciliation

2024· article· en· W7115817944 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsnot available
Fundersnot available
KeywordsThe HolocaustPerspective (graphical)MirroringAgency (philosophy)EmpathyEmbodied cognitionDialogical selfPeacebuildingNegotiation
DOInot available

Abstract

fetched live from OpenAlex

This practical theological study on intercultural reconciliation investigates peacebuilding as a community practice of Jewish-Christian engagement. With exilic meaning-making that signaled the inner wounding of Holocaust survivors, spiritual mutism became an entry point to dialogue and reconciliation in Canada. For a broad perspective on the victim-centric phenomenon, a lens of cultural trauma was used in analysis of empirical and historical data for locating the empathy and inner exilic workings of child survivors in their practice with diverse people of faith. Characterized as “shared space,” intercultural reconciliation emerged from trauma-informed religion. The Holocaust marked a pivotal period in world history and a turning point in Christian-Jewish relations. Starting in 1960, child survivors participated in the first ecumenical community organization after the Holocaust. Within two years of its civil rights initiative, Christian-Jewish Dialogue of Toronto (CJDT) was incorporated by the Anglican Church of Canada. Contributions to greater belonging with support for survivor agency facilitated their healing from cultural trauma. In bearing “witness” to the Holocaust, CJDT participants saw lives transformed with the post-traumatic growth and a legacy of embodied mercy, truth and reconciliation in the voices of survivors.

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)0.007
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.030
Scholarly communication0.0080.005
Open science0.0020.010
Research integrity0.0020.003
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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
GenreOther

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

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