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Record W3023784461 · doi:10.1515/9780773556522

Strange Trips: Science, Culture, and the Regulation of Drugs

2019· book· en· W3023784461 on OpenAlexaboutno aff
Lucas Richert

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architecturePolitical scienceEngineeringTransport engineering

Abstract

fetched live from OpenAlex

Drugs take strange journeys from the black market to the doctor's black bag. Changing marijuana laws in the United States and Canada, the opioid crisis, and the rising costs of pharmaceuticals have sharpened the public's awareness of drugs and their regulation. Government, industry, and the medical profession, however, have a mixed record when it comes to framing policies and generating knowledge to address drug use and misuse. In Strange Trips Lucas Richert investigates the myths, meanings, and boundaries of recreational drugs, palliative care drugs, and pharmaceuticals as well as struggles over product innovation, consumer protection, and freedom of choice in the medical marketplace. Scrutinizing how we have conceptualized and regulated drugs amid the pressing and competing interests of state regulatory bodies, pharmaceutical and for-profit companies, scientific researchers, and medical professionals, Richert asks how perceptions of a product shift - from dangerous substance to medical breakthrough, or vice versa. Through close examination of archival materials, accounts, and records, he brings substances into conversation with each other and demonstrates the contentious relationship between scientific knowledge, cultural assumptions, and social concerns. Weaving together stories of consumer resistance and government control, Strange Trips offers timely recommendations for the future of drug regulation

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 categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.912
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.001
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 head0.007
GPT teacher head0.232
Teacher spread0.226 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2019
Admission routes1
Has abstractyes

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