Skip to main content

Week 2: Personal drives

 Week 2: Why do I need to write this book?

I want to thank everyone who has supported my book project on just transitions with their time and money. I’d like to share my motivation for writing this book, which is further developed in the drafts of chapters 1 and 2 (currently under review!).

My drive comes from fear and frustration with our current path. We’re witnessing environmental collapse—rivers running dry, forests dying from heat, and farmers struggling with no yields. The climate crisis is here, escalating rapidly. The social contract between capital and labor feels more broken than ever, with stagnant wages, rising inflation, and a growing appeal of far-right rhetoric over progressive agendas.

But it’s not just fear that motivates me—it’s anger at the injustice and exploitation of people and nature. Despite these challenges, hope persists. Alternatives exist, often from places deemed "undeveloped" or "poor," where communities steward life in ways that inspire a better, more just world.

Our institutions, politics, and education systems have largely failed us, often perpetuating myths about development and poverty that favor those in power. It's time to rewrite this narrative—not through the lens of the powerful but by those working to expose the truth and push for change. My book aims to explore why we are in crisis, what alternatives exist, and how we can make them a reality.

I’m grateful for the support from my supervisors, Ekaterina, and many colleagues who inspire me daily. This book strives to be published with the highest editorial and sustainability standards, and any financial support is greatly appreciated. I’ll continue to share content so you can enjoy sneak peeks before the official publication. Thank you for believing in this project!

 

 


Comments

Popular posts from this blog

FastAI Deep Learning Journey Part 7: Calculating crowd size using image regression, a potential application for train use

In the previous post we show how to use a more general approach for the case when images may have one, multiple or any label at all. In this post, we will show how very little changes are required to implement computer vision deep learning methods for regression problems. To make things less theoretical, we picked a very interesting data set, containing 2000 images from people in a shooping mall. Each picture has been carefully labeled, where we can find between 12 to 60 people. This could be a very interesting application for example for public transport usage, as the extension of monthly tickets may very hard to track the usage of each train/bus or other service in real life. We will show that with only 3 epochs /1GPU we managed to get ~2 MAE (mean absolute error) or in order words, get the counting wrong 2+/- person, which is really not a lot considering we can have 60 people in the image. Let see in detail what needs to be changed and explain a potential usage for public transport ...