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Record W2963176181 · doi:10.1111/socf.12520

A Social Fields Theory of Pilgrimage: African American Christians in Israel and Palestine

2019· article· en· W2963176181 on OpenAlexfundno aff
Roger Baumann

Bibliographic record

VenueSociological Forum · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaYale University
KeywordsPilgrimageNegotiationSociologyLand of IsraelPoliticsFundamentalismPraxisJudaismGender studiesSocial sciencePolitical scienceHistoryLawAncient historyArchaeology

Abstract

fetched live from OpenAlex

What is pilgrimage and how should we understand its social significance? Traditional analyses of religiously motivated travel have focused on praxis and ritual. Contemporary analyses have further analyzed pilgrimage as a site where religion, politics, economy, and cultural production converge. This article argues that such convergence is best understood as the product of overlapping and interacting social fields and that particular pilgrimages—as sites of contestation and negotiation—take shape in the interstitial spaces between fields. Using the case of African American Christian pilgrimage to Israel and Palestine as the “Holy Land,” this article examines the relationship between overlapping fields and the negotiation of competing interests on pilgrimage. It suggests cultural framing work as a mechanism by which actors manage tensions between these fields and their interests.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.028
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.305
Teacher spread0.288 · 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
Published2019
Admission routes1
Has abstractyes

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