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Museums, Expositions, and Religion in North America since the 19th Century

2017· reference-entry· en· W2756898679 on OpenAlexaff
Hillary Kaell

Bibliographic record

VenueOxford Research Encyclopedia of Religion · 2017
Typereference-entry
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsConcordia University
Fundersnot available
KeywordsIndigenousMediationMovie theaterEthnographyPeriod (music)SociologyHistoryAestheticsVisual artsMedia studiesAnthropologyArtArt historySocial science

Abstract

fetched live from OpenAlex

Abstract Since the mid-19th century increasing numbers of North Americans have had access to new technologies of display that feature religious artifacts. Missionaries and museum curators played an especially important role as cultural brokers in this regard. They often worked together to set up ethnographic collections, although their respective goals differed in terms of spiritual uplift and public education. In the same period, the mediation of religious objects took place in other arenas too, such as recreations of sacred sites and spirit photography. In the 20th century, religious objects were mediated through cinema and television. In each case, the materialization and mediation of religion raises a number of significant questions, including those related to the aestheticization of sacred objects in public museums and the display of things and rituals associated with religious “others.” Since the 1980s, North Americans have engaged in debates about whether to repatriate indigenous objects and human bones to their communities of origin. There have also been significant protests related to the provocative use of Christian imagery in contemporary art. Increasingly, scholars have also begun to recognize and study how museum spaces are more malleable than previously assumed, especially as new publics access them and may even (re)use the sacred objects they house.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.343
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2017
Admission routes1
Has abstractyes

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