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Record W125190723 · doi:10.15173/m.v1i17.758

From Nigeria to Benin: Applying a Vendor Awareness Initiative to Combat the Counterfeit Drug Trade

2012· article· en· W125190723 on OpenAlexaffvenue
Veronica Chan, Iris Lui, Grace Lun, Naushin Nagji

Bibliographic record

VenueThe Meducator · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Sociopolitical Dynamics in Nigeria
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCounterfeit DrugsCounterfeitVendorDrugBusinessMedicineEnvironmental healthPolitical scienceMarketingPsychiatryLaw

Abstract

fetched live from OpenAlex

Nigeria and Benin are both countries that have faced a widespread epidemic of counterfeit drugs. Nigeria with the use of awareness programs and the Patent Medicine Vendors protocol was able to control the problem. Benin, on the other hand, struggled with the application of this model. This article will provide a brief introduction into the issue of counterfeit drugs and then compare Nigeria and Benin’s strategies in combating this issue.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.002
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0020.002
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 head0.043
GPT teacher head0.367
Teacher spread0.324 · 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 designObservational
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

Citations4
Published2012
Admission routes2
Has abstractyes

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