They Won't Play a Lady-O on Country Radio
pudding.cool
pudding.cool
> If you listened to this station non-stop from midnight to 11:59pm today, you’d likely only hear 3 back-to-back songs by women, compared to 245 from men.
> Songs by women are already severely underplayed, making up only 11% of total plays in 2022. Even at this low rate of play, we’d expect to see 30-40 back-to-back songs by women for this same day, like in these 10 “coin flip” simulations. In reality, we see 3.
It would be nicer if they showed their work. Simulating this in Javascript with 300 random songs with 11% female artists results in:
let songs = new Array(300).fill(0).map(x => Math.random() > .11 ? 'm' : 'f');
let bb = {f: 0, m: 0};
for (let i = 1; i < songs.length; ++i) {
if (songs[i-1] === songs[i]) ++bb[songs[i]]
};
bb.pf = 100 * bb.f/(songs.length - 1);
bb.pm = 100 * bb.m/(songs.length - 1);
console.log(bb);
{f: 3, m: 240, pf: 1.0033444816053512, pm: 80.2675585284281}
with `...new Array(6474)...`:
{f: 88, m: 5123, pf: 1.3594932797775374, pm: 79.14413718523096}The "Methodology" section includes a link to the data and code:
Usage is "./bb.pl num_songs percent_by_women". E.g., "./bb_pl 100000 11" would do a 100000 song playlist where 11% of the songs are by women. Here was the output of a run with those arguments:
79227 MM, 1228 FF
9772 MF, 9772 FM
79.23% MM, 1.23% FF
9.77% MM, 9.77% FF
The second pair of output lines gives those as percentages of total songs.
#!/usr/bin/env perl
use strict;
use List::Util;
my $T_plays = shift @ARGV || die "Args: num_songs percent_by_women\n";
my $F_percent = shift @ARGV || die "Args: num_songs percent_by_women\n";
my $F_plays = int($T_plays * $F_percent / 100 + 0.5);
my $M_plays = $T_plays - $F_plays;
my @slot;
push @slot, 'M' foreach 1..$M_plays;
push @slot, 'F' foreach 1..$F_plays;
@slot = List::Util::shuffle @slot;
my $prior = '';
my $MM = 0;
my $FF = 0;
my $MF = 0;
my $FM = 0;
foreach (@slot) {
if ($_ eq $prior) {
if ($_ eq 'M') { ++$MM; }
else { ++$FF; }
} elsif ($prior ne '') {
if ($_ eq 'M') { ++$FM; }
else { ++$MF; }
}
$prior = $_;
}
print "$MM MM, $FF FF\n";
print "$MF MF, $FM FM\n";
print "\n";
printf "%.2f%% MM, %.2f%% FF\n", 100*$MM/$T_plays, 100*$FF/$T_plays;
printf "%.2f%% MM, %.2f%% FF\n", 100*$MF/$T_plays, 100*$FM/$T_plays;This wasn't talking about the percent of songs that are women's songs and back to back, as your example.
The visual and accompanying text were using the number of all women plays, not just the back-to-back ones.
It’s more likely that the simulation back-to-back numbers would fall between 7—17 a day NOT the originally stated 30–40. For the particular station in the intro, San Antonio (KCYY-FM), the number of women’s back-to-back plays that they actually played isn’t completely outside of the realm of possibilities if left up to chance, but we would be more likely to see higher numbers. For 17 other stations, the observed numbers do fall outside the simulation distribution. (See the histograms in the methodology section. Note that they are for the full 19 days.)
Thank y'all again for raising this issue! The data is only good as the humans behind it, and I definitely bungled this a bit.
If the first song in a run is included in the back-to-back count then my simulation seems to be matching your updated simulations. If 11% of the songs are from female artists for example I get that ~2.27% of the total plays should be part of back-to-back runs of female artists.
If the first song in a run does not count as back-to-back, that was ~1.22%.
Note that the percentages for "first doesn't count" B2B simulations come out pretty close to the B2B percentages given in the article for most stations. That could just be a coincidence, but it is probably worth double checking how you calculated the B2B percentages for the actual station data to make sure that you are using "first counts" B2B.
If the station stats are using "first counts", then it looks like most of the stations are playing about 1/2 as many female artist songs B2B as they would if scheduling were random. But if the station stats are using "first doesn't count", then it looks like most are consistent with random playlists.
We’ve removed the simulation section from the intro, made it clear what data the simulations use further into the story, and added a correction. Thank you again for keeping us honest.
I think the article needs to describe the population of overall songs to support this claim. What is the percentage of men, women, mixed gender, transgender, sexual orientation, and race? Are there many top 40 hits for country music songs performed by women? Or by LGBTQIA’s?
This seems like a simple comparison before claiming inquity. The closest is when they talk about a peak of female sung songs of 33% and a valley of 11%. But what’s the overall and what is the breakdown of songs that are likely to be played?
There aren’t many black country music singers so I think a station playing few songs is actually quite equitable. Same for sexual orientation. I don’t think that it’s very smart to spread overall total population characteristics over a specific non-random selection.
Just because there are 0.0% songs played by lgbtqia country artists doesn’t mean it’s a lack of equity. Popularity is not equitable by definition since it tends to focus attention on a few acts (Matthew principle in effect [0]) so it seems odd to talk about such a thing without more context and detail.
Just because the population is 50% female doesn’t mean that 50% of country songs should be sung by women or that 50% of country music radio stations will be women. There is bound to be variation just because country music isn’t the same as the overall population.
I wouldn’t say that the Tony awards is inequitable because the gender and sexual orientation of nominees and viewers doesn’t match the national demographics.
I also think that, in context, this is not exactly surprising. Country has been political for a while, a fair amount of people have been blackballed from country like the Chicks (nee. the Dixie chicks), and performers who don’t align with conservative values in country get a lot of heat (e.g. Kacey Musgraves, Beyonce performing at the CMAs): https://www.vox.com/platform/amp/culture/2016/11/4/13521928/...
And the entire notion of ‘equity’ is the current notion of ‘woke’ for others.
I expect that conservative people are also split 50% women just like most other large groups.
And all that Twitter/public partisanship seems (at least to me) even more skewed against women, whether it's right wing or left-wing. And more authoritative right, it's between 5 and 10% (but women in those groups seemed to have a lot of power a few years ago). Here the source is 'trust me bro', but I used to be in an antifa surveillance group. We documented privately people going to far right meeting/exercises, some of us infiltrated the groups, and if the information was interesting, contacted journalist with the information (the last big 'revelation' about neonazis in the French army was from a group similar to mine). The 90/10 split male/female friends is what we noticed in meetings (in bars it was 95/5). Which was weird because it roughly matched the street occupation groups like LJG (watch groups were close to 50/50).
[0] https://news.gallup.com/poll/120839/women-likely-democrats-r...
On a totally unrelated note... if anybody knows how to better cool an HP Elitedesk 705 G4 with a 2400GE inside, I'm all ears.
I also have no idea how they selected that title; it's kind of catchy, but Lady-O is just an odd made-up word.
I remember a long time ago there was a website that you could input an FM radio station (in the US) and time and it would tell you what song was played. It was really cool, but as far as I know it shut down and never came back.
Does anybody remember this?