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Record W1576834437 · doi:10.1080/15505340903393195

Oracy in the New Millennium: Storytelling Revival in America and Bhutan

2010· article· en· W1576834437 on OpenAlexaboutno aff
Joseph Sobol

Bibliographic record

VenueDigitalCommons - WayneState (Wayne State University) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsStorytellingLiteracyAmateurOral historySociologyAestheticsNarrativeDanceHistoryMedia studiesPolitical scienceVisual artsPedagogyArtLawAnthropologyLiterature

Abstract

fetched live from OpenAlex

Starting in the 1970s there has been a significant wave of storytelling revivalism in the United States, Canada, and across much of Western Europe. Drawing on earlier revivals of oral traditional or "folk" music, dance, and crafts, this revival has spawned a new class of freelance professional storytellers along with a broader network of enthusiasts who make use of oral stories as tools in a variety of amateur and applied professional settings (especially education, business, ministry, and health care). Because this revival has taken root in an advanced technological society with a longstanding commitment to (if not actual realization of) universal literacy, it occupies a cultural position that blends conservative and radical elements. Storytellers affirm their commitment to traditional values of community memory, interconnectedness, localism, and ethnic heritage, while at the same time placing these attitudes in the service of potentially hegemonic, homogenizing forces. This paper will explore these paradoxical forces at work in the American storytelling movement and reflect on their implications for emergent storytelling work in the context of the Kingdom of Bhutan.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.013
Scholarly communication0.0090.006
Open science0.0010.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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