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Record W2085957376 · doi:10.3167/ej.2009.420210

Singing in Yiddish about London: 1880-1940

2009· article· en· W2085957376 on OpenAlexaboutno aff
Vivi Lachs

Bibliographic record

VenueEuropean Judaism · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsSingingYiddishHistoryMeaning (existential)PavilionComicsJudaismLiteratureArtArt historyVisual arts

Abstract

fetched live from OpenAlex

Singing in Yiddish about London: 1880-1940 is the story of six Yiddish songs that tell of mainly East End people and places and experiences; these snippets of history give insights into what was happening in London at the time. They tell of poverty and work, of street life and of love. They tell of characters; an old fiddler, a bagel seller, a prostitute. They tell of places, the Pavilion Theatre, Victoria Park, Morgan Street. They sparkle with life, whether deeply moving or comic. This article explores Jewish history through the songs, as well as exploring the history of the songs themselves. The songs were collected in Denmark, Canada, Germany, Liverpool and London. The article describes some of the people who sung them, who collected them and who wrote them. There is a lot unknown about the songs and why they were written, so there is much to conjecture by London Yiddishists and folk collectors. These answers throw more light onto the politics and issues of the day. Today these songs are being performed by Vivi Lachs and Klezmer Klub, a London-based band who are seeking to revive them and imbue in them a sense of their meaning for today.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.103
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.020
GPT teacher head0.195
Teacher spread0.175 · 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 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
Published2009
Admission routes1
Has abstractyes

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