Week 3: Why is this book necessary?
After studying degrowth and ecological economics for nearly 10 years, I’ve found that most of the literature focuses on:
1. Explaining why capitalism is unjust, dangerous, and at the root of multiple crises.
2. Highlighting alternatives that are more democratic and just, even if they aren’t nation-wide systems.
Regarding
(1), much of the focus is on averages, often portraying a class-free
world where some places are rich, and others are poor, neglecting
distribution differences within countries, class disparities, and the
internationalization of capital. The Global North and South narrative,
while useful for discussing structural inequalities, falls short in
fostering a global working-class consciousness. We must address colonial
arrangements and engage the working class across the globe.
Regarding
(2), it’s essential to showcase "nowtopias"—places where time is
abundant, poverty is absent, and coexistence with other living beings
has thrived for millennia. However, the critical question remains: how
do we create our own utopia, and how do we navigate toward it? We need
theories of transformation and tools for transition to design, organize,
and implement democratic alternatives within planetary boundaries.
I’m building upon three key books:
- Degrowth and Strategy: How to Bring about Social-Ecological Transformation(2020)
- Deep Transformations: A Theory of Degrowth (2024)
- Towards a Political Economy of Degrowth(2019)
These books help, but we also need:
- A toolbox to understand what has worked historically to make transitions mainstream.
- A theory of change that centers on class as a key transformation element.
- Alternatives embedded in a new economic system rooted in human and nature rights.
This
book will help you design, organize, and implement the future you
envision. We all know a just, free, and beautiful world is possible; we
need to believe in it again and relearn the art and science of
transition-making. This book provides tentative answers to these
questions, which is why it’s important to bring it to light.
If
you, like the many who already support this project, believe in its
importance, please support its publication in multiple languages with
the highest sustainability standards.
Thanks to Diana Kobus, Eva, Juanjo, Stella Martinez McShera, Oscar, Ivan Golenko, Ivan Golenko, and many others for your support. Your energy keeps me going.
With love and solidarity. Please support if you care.
When the images one is working with are of high resolution, or the demands of the application allow for very little margin of error (autonomous driving, medical analysis) it makes sense to consider high resolution image translation. This post aims to summarize the following paper: 1711.11585.pdf (arxiv.org) High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Conditional GANs have been proven to provide fairly good translations from sketches to images, but its applications have been limited to lower resolution images. The following paper develops and algorithm that works on high resolution image translation 2048 × 1024 with very realistic generation. Introduction Creating realistic representations of the world is quite expensive computationally if every dimension and detail have to be model explictely. It becomes very necessary to find low weight approaches that could represent reality very realistic from a simple abstraction as an input...

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