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Record W2939220700 · doi:10.1162/jcws_a_00856

Praying for Justice: The World Council of Churches and the Program to Combat Racism

2019· article· en· W2939220700 on OpenAlexaboutno aff
Kate Burlingham

Bibliographic record

VenueJournal of Cold War Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRacismColonialismProtestantismPolitical scienceWorld War IIChristianityEconomic JusticeWhite (mutation)LawSociologyGender studiesCriminologyReligious studies

Abstract

fetched live from OpenAlex

In the late 1960s and early 1970s, individuals around the world, particularly those in newly decolonized African countries, called on churches, both Protestant and Catholic, to rethink their mission and the role of Christianity in the world. This article explores these years and how they played out in Angola. A main forum for global discussion was the World Council of Churches (WCC), an ecumenical society founded alongside the United Nations after World War II. In 1968 the WCC devised a Program to Combat Racism (PCR), with a particular focus on southern Africa. The PCR's approach to combating racism proved controversial. The WCC began supporting anti-colonial organizations against white minority regimes, even though many of these organizations relied on violence. Far from disavowing violent groups, the PCR's architects explicitly argued that, at times, violent action was justified. Much of the PCR funding went to Angolan revolutionary groups and to individuals who had been educated in U.S. and Canadian foreign missions. The article situates global conversations within local debates between missionaries and Angolans about the role of the missions in the colonial project and the future of the church in Africa.

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)0.003
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.009
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.111
GPT teacher head0.308
Teacher spread0.197 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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