The Gross Developmental Potential (GDP2): a new approach for measuring human potential and wellbeing
Bibliographic record
Abstract
Many factors influence the health and well-being of children and the adults they will become. Yet there are significant gaps in how trajectories of healthy development are measured, how the potential for leading a healthy life is evaluated, and how that information can guide upstream policies and investments. The Gross Developmental Potential (GDP2) is proposed as a new capabilities-based framework for assessing threats to thriving and understanding progress in achieving lifelong health and wellbeing. Moving beyond the Gross Domestic Product's (GDP) focus on economic productivity as a measure of progress, the GDP2 focuses on seven essential developmental capabilities for lifelong health and wellbeing. The GDP2 capability domains include Health -living a healthy life; Needs-satisfying basic human requirements; Communication-expressing and understanding thoughts and feelings; Learning-lifelong learning; Adaption -adapting to change; Connections -connecting with others; and Community -engaging in the community. The project team utilized literature reviews and meetings with the subject and technical experts to develop the framework. The framework was then vetted in focus groups of community leaders from three diverse settings. The community leaders' input refined the domains and their applications. This prototype GDP2 framework will next be used to develop specific measures and indices and guide the development of community-level GDP2 dashboards for local sense-making, learning, and application.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".