Being trauma-informed in schools means being informed about and sensitive to trauma, and providing a safe, stable, and understanding environment for students and staff.
#traumainformedKE
Instead of caning our kids can we start to understand the science of the #PairofACEs and begin to develop #traumainformed practices for our schools, our teachers and our children.
#traumainformedKE @GSNAfrica
Magoha wants caning reintroduced in schools to tame indiscipline https://t.co/TAVmPiJrs4
— Nation Africa (@NationAfrica) January 29, 2021
Being trauma-informed in schools means being informed about and sensitive to trauma, and providing a safe, stable, and understanding environment for students and staff.
#traumainformedKE
#Traumainformed schools have become essential to successfully addressing the broadening spectrum of #ACEs globally. @GSNAfrica
First what if all our teachers started to see young people, not as 'bad' and 'indisciplined' kids - but start to ask "What has happened to this child?" or "What kind of life has this child had?"
#traumainformedKE
Promoting the #traumainformed school approaches in Kenya has the greatest potential to #positively impact all students, regardless of their traumas.
Trauma and toxic stress impact learning, behavior, and relationships at home, in school, and in the larger community.
Caning children will only cause more trauma not reduce it.
#traumainformedKE
It is not what is wrong with the child but what has happened to him.
#traumainformedKE
How can a senior public officer say publically the answer to our schooling crisis is violence?
More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
