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Record W4210616892 · doi:10.3386/w29695

The Social Consequences of Traditional Religion in Contemporary Africa

2022· report· en· W4210616892 on OpenAlexaff
Etienne Le Rossignol, Sara Lowes, Nathan Nunn

Bibliographic record

VenueNational Bureau of Economic Research · 2022
Typereport
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of British Columbia
FundersHarvard University
KeywordsSociologyGeography

Abstract

fetched live from OpenAlex

In most of Africa, traditional supernatural beliefs, including beliefs in witchcraft, black magic, or fetishism, are widespread and have remained so despite the spread of Christianity.The effects of these beliefs remain unclear.Some have hypothesized that these beliefs are beneficial and help to sustain cooperative behavior in a setting where the state is often absent.Others have documented that, inconsistent with this argument, such beliefs are negatively associated with economic and social wellbeing.We contribute to a better understanding of the causal effects of traditional supernatural beliefs by using lab-in-the-field experiments in the Democratic Republic of the Congo.Participants complete a range of experimental tasks where one player chooses whether to act in a prosocial manner towards another player.Participants are randomly assigned to another player that has either strong or weak traditional supernatural beliefs, and this information is known by the players.We find that participants act less prosocially towards randomly-assigned partners who have stronger traditional beliefs.We find that antisocial behavior is viewed as being more acceptable when it is directed towards those with stronger traditional beliefs.Consistent with both of these effects, we also find that individuals hold a wide range of negative perceptions and stereotypes about those holding strong traditional beliefs.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.561
GPT teacher head0.528
Teacher spread0.032 · 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 designObservational
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

Citations15
Published2022
Admission routes1
Has abstractyes

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