Imagining a future in global health without visa and passport inequities
Bibliographic record
Abstract
There is a growing and overdue recognition in recent years of visa and passport inequities as a significant barrier to global health education, practice, and participation. These inequities largely impact low-and middle-income country (LMIC) citizens, refugees, and asylum seekers, as highlighted by us, other affected individuals, and advocates for systemic change Examples illustrate a range of challenges including structurally discriminatory border control policies by high income countries (HICs), poor administrative due diligence in visa processes, lack of professionalism and inconsistency in assessment procedures during visa interviews or at customs, disregard for extended visa processing timelines by event organizers, associated high costs and ignorance of visa challenges by event organizers, academic institutions, and organizations While highest level of restrictions are imposed by countries in the Global North, it is crucial to note that visa and passport discrimination against LMIC citizens are practiced by countries in the Global South as well
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.016 | 0.046 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".