A Seat at the Table: Representation of Global Neurosurgery in the G4 Alliance
Bibliographic record
Abstract
The Global Alliance for Surgical, Obstetric, Trauma and Anaesthesia Care (G4 Alliance, http://www.theg4alliance.org/) is thepreeminent global surgery advocacy organization (1) dedicated to eliminating disparities in surgical care around the world. A coalitionof over 60 non-profit organizations, professional societies, academic centers, and other groups, the G4 Alliance represents civil societyinterests in global surgery from the grassroots to the international level. The organization convenes its membership to address commoninterests and concerns, facilitates knowledge and resource sharing, builds consensus and alignment, and conducts outreach withmultilateral organizations, donors, and other global health programs and advocacy initiatives. In a sector characterized by diverseactors and fragmentation of care delivery, scholarship, and funding (2,3), the G4 Alliance provides communication and coordinationaround a common surgical and health system strengthening agenda. The purpose of this paper is to highlight the multiple organizationsthat specifically contribute to neurosurgical advocacy within the G4 Alliance.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".