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Record W7112737144

Oral Poetry (PDF)

2025· book· W7112737144 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typebook
Language
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryOral poetryContext (archaeology)Oral traditionEPICOral literatureLiterary criticism
DOInot available

Abstract

fetched live from OpenAlex

This book offers a comprehensive introduction to the vast field of 'oral poetry,' encompassing everything from American folksongs, contemporary pop songs, and Inuit lyrics, to the heroic epics of Homer, biblical psalms, and epic traditions in Asia and the Pacific. Taking a broad comparative approach, it explores oral poetry across Africa, Asia, Oceania, Europe, and the Americas. Drawing on global research, Ruth Finnegan, the author of the seminal Oral Literature in Africa, sheds light on key debates such as the nature of oral tradition, the relationship between poetry and society, the differences between oral and written forms, and the role of poets in predominantly non-literate contexts. Written from a primarily anthropological and literary perspective, this study contributes to the socio-cultural aspects of verbal art while also engaging with the literary dimensions of poetry which happens at any given moment to be unwritten. Finnegan's clear, non-technical language and extensive use of translated examples make this work accessible to a wide audience, appealing not only to sociologists and anthropologists but also to those with an interest in poetry, in comparative literature, and in global folk traditions. The re-issue of this classic study is now augmented by further illustrations and a newly written Introduction and Conclusion, situating it in the context of the contemporary study of literature.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.8820.065

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 head0.097
GPT teacher head0.363
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

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