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Record W4385760197 · doi:10.1086/726446

Sensational Ensembles

2023· article· en· W4385760197 on OpenAlexfundno aff
Ryan Lash, Meredith S. Chesson, Elise Alonzi, Ian Kuijt, Terry O’Hagan, John Ó Néill, Tommy Burke

Bibliographic record

VenueCurrent Anthropology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsFolkloreMeaning (existential)EthnographyConsumption (sociology)AestheticsDichotomyPerceptionReflexivityExperimental archaeologyAnthropologySociologyVisual artsHistoryArchaeologyArtEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Drawing on posthumanist and sensory archaeology, this paper explores ensemble practices as an alternative to Eurocentric conceptions of ritual as a heuristic of cross-cultural comparison. We identify ensemble practices as the gathering of assemblages that create meaning for participants through their comingling with evocative juxtapositions of bodies, objects, comestibles, built spaces, landscapes, and environmental forces. The sensorial intensity and allusive capacity of these ensembles heighten human perceptions of relationality, providing opportunities to reflect on relationships to other beings, materials, and forces. To illustrate the utility of this approach, we examine historic, ethnographic, folkloric, and archaeological evidence for open-air food and drink consumption—picnicking—in historic and contemporary Ireland (ca. AD 1650–present). Analysis of ceramic and glass finds associated with annual celebrations of Saint Leo’s Day on Inishark Island indicates how food, drink, and household wares featured in ensemble practices that fostered memorable and commemorative experiences of shared heritage, devotion, and commensality. This study highlights how human practices create meaning through sensorially rich and evocative ensembles that often eschew traditional dichotomies associated with ritual.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.996

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.005

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 head0.038
GPT teacher head0.285
Teacher spread0.247 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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