Tribute to an outstanding scientist, caring clinician, and wonderful friend Pui-Ying Iroh Tam
Bibliographic record
Abstract
The news of Dr Pui-Ying Iroh Tam’s passing has profoundly affected not only the global child health community but also me personally. Pui-Ying was an internationally recognized leader in global child health whose work and impact resonated far beyond her immediate surroundings. She was a pioneering researcher with a keen focus on infectious diseases in children, particularly in low- and middle-income countries (LMICs). Her commitment to improving the lives of the most vulnerable children was nothing short of inspiring, and her work continues to shape the landscape of child health today. Pui-Ying was not only an exceptional scientist, but she was also deeply dedicated to promoting equity within global health. She was a tireless advocate for reshaping partnerships in a way that allowed for true collaboration, where the voices of LMIC institutions were not just heard but empowered. She believed that these institutions should be active participants in the design and execution of health projects, ensuring that solutions were not only appropriate but genuinely reflective of the needs of the communities they served. Her calls for a restructuring of these partnerships to prioritize the voices and expertise of local institutions were a testament to her commitment to social justice and to global health reform.
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 | 0.006 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.032 |
| Insufficient payload (model declined to judge) | 0.030 | 0.024 |
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".