Fidelity in Academic Global Surgery and Research: Incorporating Trustworthiness in the Development of Research Partnerships, Infrastructure, and Policy
Bibliographic record
Abstract
Academic global surgery consists of collaborative partnerships that address surgical inequities through research, training, education, advocacy, and diplomacy. It has been characterized by increased scholastic production through global surgery publications, dedicated global surgery sessions within scientific conferences, global surgery-specific research grants, database development to support global surgery research, global surgery research fellowships, and global surgery-based academic promotion paradigms. The increased emphasis on global surgery research has been accompanied by multiple ethical challenges. This article reviews critical ethical dilemmas presented by global surgery research efforts and proposes interventions on the partnership, infrastructural, and policy levels to enhance fidelity within research partnerships.
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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 | 0.156 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".