Merck launches new grant program to boost health globally
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pharmaceutical firm Merck, known as MSD outside of the U.S. and Canada, has launched a new global grants program, Solutions for Healthy Communities, that will support the work of nonprofits dedicated to improving the well-being of underserved populations in communities around the world. According to the company, SHC will invest in strategies that are designed and led by local stakeholders to meet local health needs and priorities. Grants will cover two years of project implementation, and awards will range in size from $50,000–300,000 each. The initiative will aim to catalyze innovation and facilitate access to quality healthcare, investing in programs that serve populations that are historically underserved by the healthcare system, including black, indigenous, and other people of color; people experiencing poverty; people living in rural areas; migrant populations; people with diverse gender identities and/or sexual orientations; and people living with disabilities, Merck said. SHC grants will be available in all of the regions in which the company operates, including the U.S., Europe, the Middle East, Africa, Canada, Latin America and Asia Pacific. Priority will be given to nonprofits that operate within 50 miles of a company site, the company said. For more information about the grant program, visit https://www.merck.com.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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 it