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Record W4391687240 · doi:10.1177/0920203x241230608

Therapeutic governance in Hong Kong’s anti-narcotics war, 1959–1980s

2024· article· en· W4391687240 on OpenAlexaff
Kelvin Chan

Bibliographic record

VenueChina Information · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsMcGill University
Fundersnot available
KeywordsCorporate governancePolitical sciencePublic administrationManagementEconomics

Abstract

fetched live from OpenAlex

This article examines the medical front of the anti-narcotics war in post-war Hong Kong. Situating Hong Kong in the American global drug war, this article traces colonial Hong Kong’s development of addiction treatment and cooperation with American medical experts in conducting a controversial experiment, methadone maintenance. It argues that the colonial government constructed a new form of therapeutic governance to medicalize social issues as behavioural problems. Through large-scale rehabilitation and methadone maintenance, the colonial government claimed to transform addicts in the island colony into useful subjects and justified its radical intervention into social life. To American experts, Hong Kong was an ideal laboratory to examine the controversial treatment. Hong Kong’s methadone experiment became an international success and a model reimported into the United States in the 1980s. A medical history of the anti-narcotics war not only offers a new way to reassess the social reform in the 1970s but also helps us understand the contemporary hard-line approach against drugs in Hong Kong and post-colonial Asia.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.280
Teacher spread0.270 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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