MétaCan
Menu
Back to cohort
Record W2921509494 · doi:10.1016/j.vaccine.2019.02.062

Global vaccinology training: Report from an ADVAC workshop

2019· article· en· W2921509494 on OpenAlexaff
Philippe Duclos, Lindsay Martinez, Noni E. MacDonald, Edwin J. Asturias, Hanna Nohynek, Paul‐Henri Lambert

Bibliographic record

VenueVaccine · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersUniversiteit LeidenUniversity of OxfordBill and Melinda Gates Foundation
KeywordsTraining (meteorology)Computer scienceKnowledge managementBest practiceEngineering managementEngineeringPolitical science

Abstract

fetched live from OpenAlex

At a workshop on 7-8 November 2018 the leaders of 26 advanced vaccinology courses met to carry out an extensive review of the existing courses worldwide, in order to identify education gaps and future needs and discuss potential collaboration. The main conclusions of the workshop concerned: opportunities for strengthening and expanding the global coverage of vaccinology training; evaluation of vaccinology courses; updating knowledge after the course; how to facilitate post-course 'cascade' training; developing and sharing best practices; the application of online and innovative approaches in adult education; and how to reduce costs and facilitate wider access to vaccinology training. The importance of collaboration and information exchange through networks of alumni and between courses was stressed. A web platform to provide information about existing courses for potential applicants is needed. Lack of sustainable funding is a constraint for vaccinology training and needs to be addressed.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0020.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.005

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 head0.032
GPT teacher head0.335
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 designNot applicable
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

Citations12
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueVaccineSame topicVaccine Coverage and HesitancyFrench-language works237,207