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Record W3161572788 · doi:10.1086/712739

From “Papaber Errat” to “Tincture of Opium”: Poppies, Opiates, and Pain in Early Modern Scotland (ca. 1664–1785)

2021· article· en· W3161572788 on OpenAlexfundno aff
James H. Mills

Bibliographic record

VenueThe Social History of Alcohol and Drugs · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersQueen's UniversityWellcome TrustMcGill University
KeywordsOpiumPoppyTraditional medicineApothecaries' systemPeriod (music)Opium PoppyPleasureHistoryAncient historyMedicineArtPolitical scienceLawPsychologyArchaeologyAesthetics

Abstract

fetched live from OpenAlex

This article will focus on the uses of opiates in early-modern Scotland in an effort to trace the surprisingly neglected story of just how poppy-based substances were used in this period across the British Isles. It explores their history in medical practices there, examining sources as varied as household recipes, apothecaries' invoices, and the family correspondence of surgeons alongside the medical treatises and publications of the seventeenth and eighteenth centuries. In considering the deployment of medicinal concoctions made from both the local red poppy and imported opium from the white poppy, the article argues that use of the former may have paved the way for the development of a market for the latter; the mild analgesia to be had from the rhoeadine of the red flowers had prepared medical practitioners and others for the more potent painkilling to be had from papaver somniferum products. In this light the imported opium looks less like an 'exotic' addition to the medicine of the period in Britain, and more like an enhancement drawn into existing ideas and practices. The article also concludes that, as there is no evidence to be found in these sources of anyone using the new products for intoxication, it was pain and not pleasure which drove the market for imported opium.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.216
Teacher spread0.190 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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