I have to say, of all the responses to my defense of the Feminist Glaciology piece, this one (and the others along the same lines) is not what I was expecting.

The core complaint is that the original article doesn't supply any evidence to back up its claims -- claims about colonialism, about public attitudes toward scientific expertise, about mountaineering and expeditions. That lack of evidence strikes this critic as surprising.
It shouldn't. As I noted in the original thread, the article is explicitly NOT an empirical piece. It is a review article designed to link together the empirical work of others. And there's no shame in that. On the contrary.

https://t.co/d5yPa1pNzZ
Review articles like this are both very valuable and extremely common. Common in all fields and disciplines, including in the hard sciences. I mean, there's an entire series in the social sciences devoted to just this: reviews. And it rocks.

https://t.co/UUm1Igqc0L
Here's a review article just published earlier this month. Not a whiff of original empirical evidence in it, but the authors (no doubt handsome and brilliant chaps the both of them) certainly seem confident they have something valuable to say.

https://t.co/LYb2syoJtd
So yeah: the Feminist Glaciology article doesn't supply much evidence for its claims. Probably that's partly because some of those claims are taken as obvious givens in the literature (e.g. the masculinity of colonialism), but it's also just not the purpose or goal of the piece.

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)

You May Also Like

Margatha Natarajar murthi - Uthirakosamangai temple near Ramanathapuram,TN
#ArudraDarisanam
Unique Natarajar made of emerlad is abt 6 feet tall.
It is always covered with sandal paste.Only on Thriuvadhirai Star in month Margazhi-Nataraja can be worshipped without sandal paste.


After removing the sandal paste,day long rituals & various abhishekam will be
https://t.co/e1Ye8DrNWb day Maragatha Nataraja sannandhi will be closed after anointing the murthi with fresh sandal paste.Maragatha Natarajar is covered with sandal paste throughout the year


as Emerald has scientific property of its molecules getting disturbed when exposed to light/water/sound.This is an ancient Shiva temple considered to be 3000 years old -believed to be where Bhagwan Shiva gave Veda gyaana to Parvati Devi.This temple has some stunning sculptures.