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Record W4413984236 · doi:10.15353/cfs-rcea.v12i2.747

Review of Hopped Up: How Travel, Trade, and Taste Made Beer a Global Commodity

2025· article· en· W4413984236 on OpenAlexaffvenue

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTasteCommodityCommerceAdvertisingBusinessFood scienceChemistry

Abstract

fetched live from OpenAlex

Jeffrey M. Pilcher’s Hopped Up serves up a finely crafted global history of beer, uncovering how transnational flows of people, knowledge, and technology have continually shaped and re-shaped beer’s meanings and material forms. With a keen eye for invented tradition, the book uncovers contemporary beer styles as ever-evolving products of capitalist development. Pilcher weaves a remarkable narrative spanning centuries and continents, allowing his historical account to avoid several pitfalls of previous synchronic and national-level analyses. This book will appeal to a wide audience across the social sciences and humanities, and will be especially valuable to those interested in cultural and political economy, imperialism and nation-building, and social distinction. In providing an expansive yet concise history of a ubiquitous commodity, Hopped Up serves as a shining reminder of how everyday articles of food and drink have participated in the making of the modern world.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.249
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes2
Has abstractyes

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