MétaCan
Menu
Back to cohort

The Mexican Experience of the NAPAPI Revision Process

2016· article· en· W2368347404 on OpenAlexaffabout
María Esther Martínez

Bibliographic record

VenueContexto Internacional · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsCarleton University
Fundersnot available
KeywordsNegotiationPandemicPlan (archaeology)Political sciencePublic administrationEconomic growthProcess (computing)Coronavirus disease 2019 (COVID-19)GeographyEconomicsInfectious disease (medical specialty)MedicineLaw

Abstract

fetched live from OpenAlex

In 2007, Mexico, the USA and Canada signed the North America Plan for Avian and Pandemic Influenza (NAPAPI). During the 2009 H1N1 pandemic, the plan was implemented for the first time. After the emergency, the three countries decided to review their response, and update the plan. This study analyses the trinational negotiations towards the amended NAPAPI of 2012. More specifically, it focuses on the intergovernmental synergies and intersectoral dynamics in Mexico's domestic policy-making process relevant to the negotiations. The general research questions guiding this analysis were: how do domestic intergovernmental processes and intersectoral dynamics in Mexico affect the crafting of foreign policy? And how does international cooperation affect the domestic public health agenda? The study seeks to answer these questions by examining the H1N1 pandemic, the challenges facing Mexico in the course of the pandemic, and its experience of NAPAPI. It also examines the domestic policy process in Mexico for revising this trinational plan.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.006
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.115
GPT teacher head0.526
Teacher spread0.411 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueContexto InternacionalSame topicPublic Health Policies and EducationFrench-language works237,207