Social Registries for Social Assistance and Beyond : A Guidance Note and Assessment Tool
Bibliographic record
Abstract
This paper makes several contributions. \n First, it presents a ‘guidance note’ on the framework for \n Social Registries, anchoring the definition of these systems \n in their functions along the Delivery Chain and their social \n policy role as inclusion systems, while clarifying \n terminology in a manner that is consistent with IT standards \n in the discussion of their architecture as information \n systems. Second, it illustrates the diverse typologies and \n trajectories of country experiences with Social Registries \n with respect to their (a) institutional arrangements \n (central and local); (b) use as inclusion systems (coverage, \n single or multi-program use, static or dynamic intake and \n registration); and (c) structure as information systems \n (structure of data management; degree and us of \n interoperability with other systems). These patterns \n primarily derive from a review of Social Registries in a \n sample of 20 countries), (Azerbaijan, Brazil, Chile, China, \n Colombia, the Dominican Republic, Djibouti, Georgia, \n Indonesia, Macedonia, Mali, Mauritius, Mexico, Montenegro, \n Pakistan, the Philippines, Senegal, Sierra Leone, Turkey, \n and Yemen). The paper also draws on experience in other \n countries (Kenya, Rwanda, Nigeria, Egypt, Jordan, Vietnam, \n India, Estonia, Belgium, the US, Canada, Australia, and \n others) to illustrate specific points. Third, this paper \n develops a basic ‘Assessment Tool’ covering the core \n building blocks of Social Registries using a ‘checklist’ \n style of questions. Given the wide diversity of Social \n Registries in both their role in social policy and in their \n architecture, the approach is not prescriptive: it does not \n advocate for any specific model or blueprint for Social \n Registries. Any diagnostics or recommendations that emerge \n from use of this Guidance Note and Assessment Tool will be \n country specific. Some key take-away messages include: (a) \n the importance of recognizing both the role of the ‘front \n lines’ for outreach, intake and registration (Social \n Registries as inclusion systems) and the ‘back office’ \n functions of Social Registries as information systems; (b) \n the potential power of Social Registries as integrated and \n dynamic gateways for inclusion; (c) the recognition that \n Social Registries are generally part of end-to-end systems \n for specific programs, integrated social protection \n information systems, and/or even ‘whole-of-government’ \n approaches; and (d) there is significant diversity in the \n typology and trajectories of Social Registries across \n countries and over time.
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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 | 0.116 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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 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".