MétaCan
Menu
Back to cohort
Record W2939996017 · doi:10.3390/heritage2020071

Speaking Stones: Oral Tradition as Provenance for the Memorial Stelae in Gujarat

2019· article· en· W2939996017 on OpenAlexaff
Durga Kale

Bibliographic record

VenueHeritage · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVenerationNarrativeIdentity (music)Oral literatureOral historyOral traditionHistoryState (computer science)ProvenanceArchaeologyAncient historyAnthropologyArtSociologyLiteratureAesthetics

Abstract

fetched live from OpenAlex

Anthropological fieldwork in rural settlements on the west coast of India has unraveled the close connection between lived experiences, spaces and objects. These “inalienable possessions”, in the words of Annette Weiner, help reconstruct the past through the supplementation of oral traditions. Following this vein, the paper attempts to mesh together the material culture and oral histories to establish the provenance for the plethora of memorials in the state of Gujarat. A series of oral narratives collected in Western India since 2014 has highlighted the role of medieval memorial stelae that commemorate the deceased heroes of war and their wives and companions. This paper creates a niche for the Gujarati oral tradition as provenance for the continued veneration of these memorials. Field observations from 2014–2016 and notes from research in Gujarat from 1985 onwards enabled the study of patterns in the oral preservation of literature. A systematic documentation of the existing stelae and associated oral traditions has informed the views in this paper. The paper speaks to all levels of interaction and the making of an identity for the memorial stones that are unique to the state of Gujarat. A case for the inclusion of such rich material in museum displays is made in connection with this case study of the memorial stelae in Gujarat.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.336
Teacher spread0.288 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHeritageSame topicAnthropological Studies and InsightsFrench-language works237,207