Is The Gender Critical Movement a Cult?

Looking back on her time in the “gender critical” feminist movement, [Amy Dyess] is unequivocal: it’s a cult.

A cult that groomed her when she was vulnerable and sleeping in her car; a cult that sought to control her, keeping tabs on her movements and dictating what she could and couldn’t say; a cult that was emotionally and sexually abusive towards her.

As Amy began to notice more and more red flags about the GC movement – like how it defended abusive women, how it wouldn’t let lesbians speak out about sexual assault perpetrated by women, and how it was forming alliances with homophobic groups – she started asking questions.

I definitely stuck a pin in this article when it popped up in my feeds. And yes, it’s old news by now, but I’m surprised so few people have discussed the central conceit: is the Gender Critical movement a cult?

[CONTENT WARNING: TERFs, sexism]

[Read more…]

Fundraising Update 1

TL;DR: We’re pretty much on track, though we also haven’t hit the goal of pushing the fund past $78,890.69. Donate and help put the fund over the line!

With the short version out of the way, let’s dive into the details. What’s changed in the past week and change?

import datetime as dt

import matplotlib.pyplot as pl

import pandas as pd
import pandas.tseries.offsets as pdto


cutoff_day = dt.datetime( 2020, 5, 27, tzinfo=dt.timezone(dt.timedelta(hours=-6)) )

donations = pd.read_csv('donations.cleaned.tsv',sep='\t')

donations['epoch'] = pd.to_datetime(donations['created_at'])
donations['delta_epoch'] = donations['epoch'] - cutoff_day
donations['delta_epoch_days'] = donations['delta_epoch'].apply(lambda x: x.days)

# some adjustment is necessary to line up with the current total
donations['culm'] = donations['amount'].cumsum() + 14723

new_donations_mask = donations['delta_epoch_days'] > 0
print( f"There have been {sum(new_donations_mask)} donations since {cutoff_day}." )
There have been 8 donations since 2020-05-27 00:00:00-06:00.

There’s been a reasonable number of donations after I published that original post. What does that look like, relative to the previous graph?

pl.figure(num=None, figsize=(8, 4), dpi=150, facecolor='w', edgecolor='k')

pl.plot( donations['delta_epoch_days'], donations['culm'], '-',c='#aaaaaa')
pl.plot( donations['delta_epoch_days'][new_donations_mask], \
        donations['culm'][new_donations_mask], '-',c='#0099ff')

pl.title("Defense against Carrier SLAPP Suit")

pl.xlabel("days since cutoff")
pl.ylabel("dollars")
pl.xlim( [-365.26,donations['delta_epoch_days'].max()] )
pl.ylim( [55000,82500] )
pl.show()

An updated chart from the past year. New donations are in blue.

That’s certainly an improvement in the short term, though the graph is much too zoomed out to say more. Let’s zoom in, and overlay the posterior.

# load the previously-fitted posterior
flat_chain = np.loadtxt('starting_posterior.csv')


pl.figure(num=None, figsize=(8, 4), dpi=150, facecolor='w', edgecolor='k')

x = np.array([0, donations['delta_epoch_days'].max()])
for m,_,_ in flat_chain:
    pl.plot( x, m*x + 78039, '-r', alpha=0.05 )
    
pl.plot( donations['delta_epoch_days'], donations['culm'], '-', c='#aaaaaa')
pl.plot( donations['delta_epoch_days'][new_donations_mask], \
        donations['culm'][new_donations_mask], '-', c='#0099ff')

pl.title("Defense against Carrier SLAPP Suit")

pl.xlabel("days since cutoff")
pl.ylabel("dollars")
pl.xlim( [-3,x[1]+1] )
pl.ylim( [77800,79000] )

pl.show()

A zoomed-in view of the new donations, with posteriors overlaid.

Hmm, looks like we’re right where the posterior predicted we’d be. My targets were pretty modest, though, consisting of an increase of 3% and 10%, so this doesn’t mean they’ve been missed. Let’s extend the chart to day 16, and explicitly overlay the two targets I set out.

low_target = 78890.69
high_target = 78948.57
target_day = dt.datetime( 2020, 6, 12, 23, 59, tzinfo=dt.timezone(dt.timedelta(hours=-6)) )
target_since_cutoff = (target_day - cutoff_day).days

pl.figure(num=None, figsize=(8, 4), dpi=150, facecolor='w', edgecolor='k')

x = np.array([0, target_since_cutoff])
pl.fill_between( x, [78039, low_target], [78039, high_target], color='#ccbbbb', label='blog post')
pl.fill_between( x, [78039, high_target], [high_target, high_target], color='#ffeeee', label='video')

pl.plot( donations['delta_epoch_days'], donations['culm'], '-',c='#aaaaaa')
pl.plot( donations['delta_epoch_days'][new_donations_mask], \
        donations['culm'][new_donations_mask], '-',c='#0099ff')

pl.title("Defense against Carrier SLAPP Suit")

pl.xlabel("days since cutoff")
pl.ylabel("dollars")
pl.xlim( [-3, target_since_cutoff] )
pl.ylim( [77800,high_target] )

pl.legend(loc='lower right')
pl.show()

The previous graph, this time with targets overlaid.

To earn a blog post and video on Bayes from me, we need the line to be in the pink zone by the time it reaches the end of the graph. For just the blog post, it need only be in the grayish- area. As you can see, it’s painfully close to being in line with the lower of two goals, though if nobody donates between now and Friday it’ll obviously fall quite short.

So if you want to see that blog post, get donating!

The Expanding Colony

I owe you an update to the fundraiser, but alas I instead got addicted to watching Twitter feeds for protest info. So let’s do this instead.

The thesis of Chris Hayes’ last book was that there were two police systems in the USA: that of “The Nation,” which behaves much as you’d expect, and that of “The Colony,” which is aimed at subjugating a subset of the populace through terror and pain. Citizens of “The Nation” don’t usually see what citizens of “The Colony” see, those visions are hidden both by design and a willful blindness. In the USA, for instance, police killed 1,028 people in the last year. Most are never heard of, like Steven Taylor or Breonna Taylor, both because of the sheer number of times it happens and because we’re taught to think of these deaths as “justified.” Aggressively swing a baseball bat in a Wal-Mart? That justifies the death penalty, without trial. Suspected of having drugs and next to someone firing at the police? Death penalty, no trial. Citizens of The Nation grasp what’s happening on an intuitive level, but because they rarely face reality this knowledge is allowed to slip to the back of their minds.

Every once in a while, though, The Nation gets a glimpse of what The Colony has to live with. Being forced to confront reality can lead to changes, but often those changes are incremental or incomplete, and The Nation comes up with excuses to turn its head away again. Looting and rioting? How dare these villains break the law! If only they followed the example of Martin Luther King, Jr. [Read more…]