The Global Centre for Risk and Innovation (GCRI)
Social cohesion is the trust, shared norms and ties that let people cooperate across differences. Technology can strengthen it by connecting people and improving services, or weaken it through polarisation, surveillance, exclusion and loss of shared information. This page sets out how technology affects trust and participation, how cohesion is measured, and what evidence serves the governments and civil society organizations concerned.
GCRI · In development · service level R0
Social cohesion is the trust, shared norms and connections that let people cooperate across differences. Technology can strengthen it by connecting people and improving services, or weaken it through sorting into like-minded groups, surveillance and exclusion. Trust in institutions shapes whether people follow guidance in crises.
For technology and social cohesion, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and night lights with relief in 3D and true colour from NASA GIBS, framed on Latin America and the Caribbean; every other NASA layer, live feed and forecast waits in the layer list.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and exposure, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
For technology and social cohesion, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and night lights with relief in 3D and true colour from NASA GIBS, framed on Latin America and the Caribbean; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
Trust, misinformation and public outcomes. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Digital networks let people find others with shared interests, which can build communities and also sort people into like-minded groups.
When people believe they are watched, they may withdraw from public participation, especially groups that have been targeted before.
People without access or skills are left out of digital public services and conversations, widening social gaps.
Trust in institutions shapes whether people follow official guidance in crises and accept collective decisions they disagree with.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Generalised trust | The belief that most people can be trusted, measured in values surveys. |
| Affective polarisation | Dislike and distrust between people who identify with opposing political groups. |
| Digital exclusion | Lack of access, skills or confidence to use digital technologies and services. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Generalised trust | percent agreeing most people can be trusted | per survey | Values surveys | REPORTED |
| Trust in government | percent expressing confidence | per survey | Trust surveys | REPORTED |
| Affective polarisation | difference in feelings toward own and other groups | per survey | Election and social surveys | REPORTED |
| Civic participation | percent participating in community activities | per survey | Social surveys | REPORTED |
| Internet shutdowns | count of incidents | annual | Shutdown trackers | REPORTED |
| Digital public service use by group | percent of population | per survey | Household surveys | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| World Values Survey | Trust, values and participation across countries | Free for non-commercial use, terms to check |
| OECD Survey on Drivers of Trust in Public Institutions | Trust in government and its drivers | Public, OECD terms |
| Access Now KeepItOn shutdown data | Internet shutdown incidents | Public, terms to check |
| Afrobarometer, Latinobarómetro and other regional barometers | Public opinion on trust and governance | Free for non-commercial use, terms per survey |
| Pew Research Center global surveys | Attitudes toward technology and institutions | Public, terms per release |
Society and Human Development prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Governments | How to design digital services and policies that build trust | Inclusion reviews; trust evidence |
| Civil society organizations | Where to focus on participation and inclusion | Participation and cohesion evidence |
| Regulators | How to address online harms without restricting rights | Studies of technology effects |
Trust shapes behaviour. During epidemics and disasters, people are more likely to follow guidance from sources they trust, and trust is built or lost by how institutions act over time. Misinformation spreads through social networks, often faster than corrections, especially when it is emotionally charged or aligned with existing beliefs. Prebunking, accurate and timely information from trusted messengers and community engagement are among the approaches with supporting evidence.
Digital exclusion follows lines of age, income, disability, gender and geography. When services move online, people without connectivity, devices, skills or digital identity can lose access to benefits and information. Algorithmic systems used in public services can reproduce bias in their training data. Cohesion is also affected by perceived fairness: crises that appear to burden some groups unfairly erode solidarity.
Design choices matter. Services that keep offline and assisted channels, explain automated decisions and allow appeal tend to preserve trust and inclusion, while opaque systems that cannot be challenged erode both, even when they are efficient on average.
Surveys track trust, beliefs and behaviour; responses can differ from actions.
Analysis of how content spreads on platforms; access to platform data is restricted.
Field and online experiments test messaging and correction; effects can fade.
Limits: platform data access is uneven and changing, causal effects of exposure to misinformation are hard to isolate, and findings from one country or platform may not transfer.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.