Rapid Scoping Review on the Topic of Ensuring Social Protection and Basic Services to Inform the United Nations Framework for the Immediate Socioeconomic Response to COVID-19
Bibliographic record
Abstract
This rapid scoping review has informed the development of the UN Research Roadmap for the COVID-19 Recovery on the topic of "Ensuring Social Protection and Basic Services." The aim was to provide a robust synthesis of key concepts and existing evidence drawn from a wide range of disciplines to support the identification and appraisal of research priorities. An emergent theme has been the notion that measures implemented in response to COVID-19 merely ameliorate symptoms of entrenched, systemic gender-, age-, and race-based inequity, inequality, and exclusion. Key findings include the critical role of contextual and community-based knowledge for informing the design, development, and delivery of programs, as well as the urgent need for implementation science to move existing knowledge into action. This review also describes how the disruption associated with "shock events" such as the COVID-19 pandemic is often associated with unusually high levels of interest and willingness to invest in programs and policies to strengthen strained systems. As such, an unprecedented window of opportunity exists to leverage measures implemented in response to the COVID-19 pandemic to effect large-scale, sustainable change and thereby increase the resiliency of our interconnected systems for the future.
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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 | 0.069 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".