The Legacy of a Philanthropic Exit: Lessons From the Evaluation of the Hewlett Foundation’s Nuclear Security Initiative
Bibliographic record
Abstract
As its seven-year Nuclear Security Initiative wound down in late 2014, the William and Flora Hewlett Foundation engaged ORS Impact to conduct a summative evaluation. That evaluation yielded insights pertinent to future work on nuclear security and other fields where policy-related investments, strategies, and goals are prioritized, as well as insights regarding Hewlett’s approach to the initiative exit. During the life of the initiative, significant changes in the geopolitical landscape influenced both the relevance and the expected pace of advancement of its established goals and targets. Rather than focusing on whether identified targets had been achieved in a narrow “success/failure” framework, the evaluation explored where and how Hewlett’s investments and actions made a difference and where meaningful progress occurred over the seven years of investment. Evaluation findings highlighted contributions and areas of progress that had not been explicitly anticipated or specifically identified in the initiative’s theory of change. This article describes the initiative and its theory of change, evaluation methods and approaches, findings, and how these informed the foundation’s planning for initiative exits and approach to measurement of time-bound investments. Although time-bound philanthropic initiatives are a well-established practice, the approach merits closer examination in order to discern effective ways to implement, evaluate, and wind down these types of investments.
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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 | 0.218 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".