We prefer what we know (or what they tell us)

In the first observed session (n1717), destination preferences were shared between the Americas (47%), Asia-Australia (29%) and Europe (24%), with Africa (0%) out of radar. In the second (n14)14), Asia-Australia rose to 64%, America remained at 29% and only one response targeted Madagascar.

When it comes to rejections, India concentrates much of the reluctance (between 41% of the total and 47% of those who respond), followed by Israel. The reasons that the students associate are conflict, dictatorship, urban density, inequalities or health – a cocktail of perceptions mediated by news and recommendations.

“It’s not about ‘convincing them to like a country’, but about teaching them to look: where do my ideas come from? What data do I have? What are I missing?” summarizes the Empatitza team.



Where do rankings fail? Tracks and biases

In economic rankings, subgroups often hit the total GDP (USA, China, Germany, Japan and India), but pricked with the lowest GDP per capita: no team in the first session and only one in the second. Although four of the five poorest countries are in Africa, many teams did not locate the continent. Reading: gaps in information and generalisations about Africa.

In migrations, a part of the groups attributes by inertia mass emigration to African countries (Niger, Algeria, South Africa), when large emitters include India, Mexico, China or Russia (by demographic weights and diasporas). On the other hand, large receivers (USA, Germany, Saudi Arabia, UK, France) are more easily identified. Homelessness in rich cities (Los Angeles, New York, San Francisco) surprises and breaks the cliché “poverty global South”.



Europe and the arms business, the great blind spot

When talking about arms exports, the teams nail the top-7, but they are surprised that Spain is around the seventh place. The reaction reveals an blind spot: the invisibility of the European role in one of the most sensitive commercial chains on the planet.



And now, how do we translate it into education?

The case study does not remain in the diagnosis. The proposals that emerge are specific:

“Data that break myths”: fast graphs (total GDP vs. per capita; top migrants/immigrants; exporters/weapons importers) and mute maps to fix geography.

“Look at Africa with data”: HDI and GDP per capita maps by subregions, examples of growth and vulnerability, and comparisons with Asia and America.
It includes economic interdependence (global supply chains – critical minerals, agri-food, textiles – remittances, FDI, debt) and climate (historic responsibility for emissions vs. impacts, climate financing, losses and damage, climate migrations).

Local context: update the top of migratory origins in Catalonia and discuss why they vary.

Pre-post evaluation: two questions before and after to measure change of ideas and biases.



The holder of funds

What both sessions reveal is simple and demanding at the same time: global citizenship is learned. When students contrast data with stories, maps become more precise and, above all, more human. Empathizing is not agreeing with everyone; it is better understanding the world we share.