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Curatorial Conversations: Cultural Representation and the Smithsonian Folklife Festival

2019· article· en· W4235149473 on OpenAlexaff
Jillian Gould

Bibliographic record

VenueJournal of American Folklore · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

Book Review| January 01 2019 Curatorial Conversations: Cultural Representation and the Smithsonian Folklife Festival Curatorial Conversations: Cultural Representation and the Smithsonian Folklife Festival. Ed. Olivia Cadaval, Sojin Kim, and Diana Baird N’Diaye. (Jackson: University Press of Mississippi, 2016. Pp. 304, 71 black-and-white photographs, preface, prologue, introduction, bibliography, index.). Jillian Gould Jillian Gould Memorial University Search for other works by this author on: This Site Google Journal of American Folklore (2019) 132 (523): 89–91. https://doi.org/10.5406/jamerfolk.132.523.0089 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Jillian Gould; Curatorial Conversations: Cultural Representation and the Smithsonian Folklife Festival. Journal of American Folklore 1 January 2019; 132 (523): 89–91. doi: https://doi.org/10.5406/jamerfolk.132.523.0089 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of American Folklore Search Advanced Search The text of this article is only available as a PDF. Copyright 2019 by the Board of Trustees of the University of Illinois2019 Article PDF first page preview Close Modal You do not currently have access to this content.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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.

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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