Enhancing governance and strengthening advocacy for policy change of large Collective Impact initiatives
Bibliographic record
Abstract
Nutrition issues are increasingly being addressed through global partnerships and multi-sectoral initiatives. Ensuring effective governance of these initiatives is instrumental for achieving large-scale impact. The Collective Impact (CI) approach is an insightful framework that can be used to guide and assess the effectiveness of this governance. Despite the utility and widespread use of this approach, two gaps are identified: a limited understanding of the implications of expansion for an initiative operating under the conditions of CI and a lack of attention to advocacy for policy change in CI initiatives. In this paper, a case study was undertaken in which the CI lens was applied to the advocacy efforts of Alive & Thrive (A&T), UNICEF and partners. The initiative expanded into a regional movement and achieved meaningful policy changes in infant and young child feeding policies in seven countries in Southeast Asia. These efforts are examined in order to address the two gaps identified in the CI approach. The objectives of the paper are (a) to examine the governance of this initiative and the process of expansion from a national to a regional, multilayered initiative, with attention to challenges, adaptations, and key elements, and (b) to compare advocacy in the A&T-UNICEF initiative and in typical CI initiatives and gain insight into how the practice of advocacy for policy change can be strengthened in CI initiatives.
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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 | 0.144 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".