Social Cohesion and Community Resilience During COVID-19 and Pandemics: A Rapid Scoping Review to Inform the United Nations Research Roadmap for COVID-19 Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shock events uncover deficits in social cohesion and exacerbate existing social inequalities at the household, community, local, regional, and national levels. National and regional government recovery planning requires careful stakeholder engagement that centers on marginalized people, particularly women and marginalized community leaders. The aim of this rapid scoping review was to inform the United Nations Research Roadmap for the COVID-19 Recovery, based on Pillar 5 of the United Nations Framework for the Immediate Socioeconomic Response to COVID-19: Social Cohesion and Community Resilience. We present a summary of key concepts across the literature that helped situate this review. The results include a description of the state of the science and a review of themes identified as being crucial to sustainable and equitable recovery planning by the United Nations. The role of social cohesion during a disaster, particularly its importance for upstream planning and relationship building before a disaster occurs, is not well understood and is a promising area of future research. Understanding the applicability of social cohesion measurement methodologies and outcomes across different communities and geographies, as well as the development of new and relevant instruments and techniques, is urgently needed in the context of the global COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it