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Record W4417397275 · doi:10.1186/s12961-025-01436-3

Establishing criteria and distributing conference grants for participants from the Global South: experiences from the Canadian Conference on Global Health 2024

2025· article· en· W4417397275 on OpenAlexaffabout
Alisha Gauhar, M.J. Mutumba-Nakalembe, Colleen Davison, Michelle Amri

Bibliographic record

VenueHealth Research Policy and Systems · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsCanadian Public Health AssociationQueen's UniversityWestern UniversityBC Centre for Disease ControlUniversity of Waterloo
Fundersnot available
KeywordsScholarshipRubricHealth services researchGlobal healthPublic healthHealth policyHealth administrationInternational developmentProcess (computing)

Abstract

fetched live from OpenAlex

The annual Canadian Conference on Global Health (CCGH) provides a platform for global health professionals and students from various sectors and disciplines to share knowledge and innovations in research, policy, and practice. However, known barriers disproportionately faced by participants from the Global South (PGS) often result in a lower representation, attendance, and participation of PGS in conferences such as the CCGH. Recognizing these barriers and to promote greater conference equity, the Canadian Association for Global Health (CAGH) with funding from the International Development Research Centre created a scholarship programme for PGS to provide full and partial funding to attend the CCGH. This paper details the six-step process used for the creation of the PGS scholarship criteria, as well as considerations for selecting recipients, methods for scoring applications, and processes for distributing the scholarships. The criteria and rubric designed provides a foundation for a reproducible resource that can be scaled up for other international conferences and can be applied to other groups underrepresented in conference settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.439
GPT teacher head0.551
Teacher spread0.112 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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