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The Saints in Old Norse and Early Modern Icelandic Poetry

2018· article· en· W4249776469 on OpenAlexaboutno aff
Marianne Kalinke

Bibliographic record

VenueThe Journal of English and Germanic Philology · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsnot available
Fundersnot available
KeywordsIcelandicOld NorsePoetryPhilologyHistoryIconCitationLiteratureClassicsArtLibrary scienceLinguisticsPhilosophySociologyComputer scienceFeminism

Abstract

fetched live from OpenAlex

Book Review| January 01 2018 The Saints in Old Norse and Early Modern Icelandic Poetry The Saints in Old Norse and Early Modern Icelandic Poetry. By Kirsten Wolf and Natalie M. Van Deusen. Toronto Old Norse and Icelandic Series. Toronto: University of Toronto Press, 2017. Pp. xiv + 363. $95. Marianne Kalinke Marianne Kalinke University of Illinois at Urbana–Champaign Search for other works by this author on: This Site Google The Journal of English and Germanic Philology (2018) 117 (1): 92–93. https://doi.org/10.5406/jenglgermphil.117.1.0092 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Marianne Kalinke; The Saints in Old Norse and Early Modern Icelandic Poetry. The Journal of English and Germanic Philology 1 January 2018; 117 (1): 92–93. doi: https://doi.org/10.5406/jenglgermphil.117.1.0092 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 PressThe Journal of English and Germanic Philology Search Advanced Search The text of this article is only available as a PDF. Copyright 2018 by the Board of Trustees of the University of Illinois2018 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)0.001
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 score0.935
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.212
Teacher spread0.199 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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