Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.033 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".