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Record W4385347475 · doi:10.1558/aff.21904

Pouring the Past

2023· article· en· W4385347475 on OpenAlexaff
Marie Hopwood, Melissa M. Ayling

Bibliographic record

VenueArchaeology of Food and Foodways · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsSimon Fraser UniversityVancouver Island University
Fundersnot available
KeywordsCraftAestheticsTourismSubtextOrder (exchange)BarbarianAppropriationTranquillityHistoryArtArchaeologyLiteraturePsychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

One of the first questions asked of ancient ale recreations is whether or not the beer is ‘authentic’. In the subtext of this question is an unwavering belief that a single, original form actually existed and is attainable in a modern ale inspired by millennia-old archaeological traces. This desire for an idealized authenticity comes even before curiosity about how the beverage tastes, which begs the question: what is actually being consumed? The ‘authentic’ circumstances of ancient beer brewing differ with each archaeological investigation. Many of the earliest brews would require some form of hygienically questionable open boils in large ceramic vessels in order to be recreated, and were designed to be drunk through straws made of… straw. Under this burden of proof, an ancient-inspired ale would only be deemed genuine if not viable in a craft beer market. Yet this consumer desire for an authentic experience or product has been groomed for decades through tourism. Consumers believe that the authentic exists, is packageable, and can be purchased. Today, the question of authenticity is compounded with the serious issues of cultural appropriation, decolonizing tourism, and global, migratory work forces. All of this leads to the very real need for an exploration of authenticity in experimental and sensory archaeology. Towards that goal, we discuss authenticity and its implications for ancient beer recreations, its implications for experimental archaeology, and how to best unpack this topic for the broader audiences for whom these recreations are made.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.238

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)0.0000.000

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 head0.024
GPT teacher head0.216
Teacher spread0.192 · 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 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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