MétaCan
Menu
Back to cohort
Record W3195673944 · doi:10.3138/cbmh.488-112020

Different Peoples, Different Inebriations: The Recognition of Different Cultures of Intoxication in Early Modern English Medicine

2021· article· en· W3195673944 on OpenAlexvenueno aff
Edoardo Pierini

Bibliographic record

VenueCanadian Journal of Health History · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOpiumTurkishConsumption (sociology)Alcohol intoxicationWestern medicineAlcohol consumptionMedicineTraditional medicinePsychologyAlternative medicineEnvironmental healthSocial scienceSociologyPolitical sciencePoison controlInjury preventionAlcoholLawPathologyTraditional Chinese medicine

Abstract

fetched live from OpenAlex

In early modern Europe, the global dimensions of the drug trade and the introduction of new substances contributed to the development of new cultures of intoxication. This process was particularly evident in England, where a new intoxication culture emerged from the recognition of how different substances produced similar reactions. Medical travel literature provides a critical source for examining alternative methods of drug consumption in the non-Western world in this period: culturally embedded practices like Turkish opium eating or Native American tobacco smoking became significant benchmarks for comparing with Western habits of alcohol consumption. This article argues that the early modern Western medical community relied on comparisons of intoxication in other contexts in an effort to describe its own culturally embedded practices of alcohol intoxication.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.020
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.229
Teacher spread0.174 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Health HistorySame topicHistorical Economic and Social StudiesFrench-language works237,207