Establishing criteria and distributing conference grants for participants from the Global South: experiences from the Canadian Conference on Global Health 2024
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.098 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.047 | 0.011 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".