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Record W4391860428 · doi:10.1515/9781988111360

Engage in Public Scholarship!

2022· book· en· W4391860428 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

New folk music and folk-song materials in this comprehensive study are particularly important for singers, folk music enthusiasts, ethnomusicologists, comparative and cultural studies scholars, and those interested in Canadian culture. LaRena Clark was a great singer and knew many fine songs. Her wide repertoire covers almost the complete range of types and topics of traditional Anglo-Canadian songs. Comparison with other collections in Canada, the United States, the British Isles, and Australia indicate just how unique and far-reaching it was. Clark's background and her varied ancestry shaped her repertoire. The account of her parents' activities gives a vivid picture of folk life in rural Ontario during the early years of this century. She knew some Canadian songs previously unreported, and she wrote songs with a strong Canadian flavour. Musically, Clark's songs are a microcosm of practices characteristic of British folk music throughout the English-speaking world. Particularly noteworthy is her constant reworking of traditional materials, procedures, forms, and individual tunes.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.439
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.003
Scholarly communication0.0170.014
Open science0.0020.016
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.4390.452

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 head0.119
GPT teacher head0.332
Teacher spread0.213 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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