Bibliographic record
Abstract
Integrating social entrepreneurs into the “health for all” formula William Drayton,a Charlie Brown,b & Karin Hillhouse b This month’s theme “Knowledge trans-lation in global health ” offers an oppor-tunity to highlight the overlooked but dramatic impact of social entrepreneurs in the health sector and to detail ways in which their knowledge, innovations and enterprise can add strength and util-ity to systems ripe for change. The global health sector and its corporate, academic, governmental and philanthropic partners are fully engaged in efforts to improve basic and applied research, deliver networks and resources for more timely and better care, and design more effective mecha-nisms to bridge the gaps between knowledge and practice. This is an ambitious agenda of growing urgency, with daunting chal-lenges. Kwok-Cho Tang et al. provided the context,1 noting that since the 1986 Ottawa Charter for Health Promotion new patterns of consumption and communication, urbanization, envi-ronmental changes and public health emergencies — along with accelerating social and demographic changes to work, learning, family and community life — have become critical factors influencing health. Over the same period, Ashoka: Innovators for the Public began a global search for individuals with ideas for changing systems to make them capable of bringing about vastly improved outcomes in education, human rights, environment, economic development, civic engagement and health.2 Ashoka recognized these people as “social entrepreneurs ” and led a change in the ways that foundations and other inves-tors analyse opportunity and measure impact, that business schools prepare students for careers in the fast-growing citizen sector, and that corporate and community leaders create opportuni-ties for meeting their goals.3 Drawing on Ashoka’s 25 years of experience with 1700 social innovators in 70 countries, including some 400 in the health sector, its Changemakers
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.515 | 0.338 |
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".