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Record W4210300552 · doi:10.1080/11926422.2021.2011756

The Jab Hurts: assessing Canada's role in vaccine nationalism during COVID-19

2022· article· en· W4210300552 on OpenAlexaffabout
Emily M. Walter

Bibliographic record

VenueCanadian Foreign Policy Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsNationalismPolitical sciencePandemicDevelopment economicsCoronavirus disease 2019 (COVID-19)SovereigntyPolitical economyEconomic nationalismInternational tradeBusinessEconomicsLawMedicinePolitics

Abstract

fetched live from OpenAlex

COVID-19 has led many countries to dismiss globalization and reassert sovereignty through ‘Vaccine Nationalism’, a process whereby high-income countries race to secure vaccine doses for their own populations. This nationalist behaviour has severe consequences for low-income countries lacking vaccine manufacturing capacity or the funds to buy doses, by limiting their access to the pool of available vaccines. Despite calls for vaccine sharing by international organizations, this nationalist behaviour has continued for two years by many countries, including typical multilateral actors such as Canada. To understand this trend, this paper argues that the pursuit of vaccine nationalism by wealthy nations is both a rational by-product of realpolitik, and justified through securitization discourse. In looking back at Canada's active engagement in vaccine nationalism throughout the pandemic, this paper concludes that as a long-time multilateral leader, Canada's nationalist actions to secure COVID-19 vaccines were uncalled for.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.116
Threshold uncertainty score0.838

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0150.005
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.003
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 head0.018
GPT teacher head0.321
Teacher spread0.303 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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