2021.10.20 00:08 ThePrimeReason Finally found my first bear Honey's original outfit. It's so nice to see him wearing it again.
|submitted by ThePrimeReason to buildabear [link] [comments]|
2021.10.20 00:08 Stunning_Ask1069 am available for hookup and My services are erotic link eat & GFE&new 69 style &BBBJ+BJ no condom&body to body Nuru message & shower togetherd & kissing and touch me & B2B and I only want serious person and I don't sex for free text me on my Kik : lilianaamber or (405) 529-6302
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2021.10.20 00:08 ohbrubuh Perfect threesome?
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2021.10.20 00:08 kevlorneswath sUcK iT hEdGiEs
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2021.10.20 00:08 SirCarl0s Best loadout for a Plague Marine Fighter?
2021.10.20 00:08 freewillylovesstraws Ferda boys
2021.10.20 00:08 F4nt4zyW0rld Melissa Santos
|submitted by F4nt4zyW0rld to WrestleFeetTwo [link] [comments]|
2021.10.20 00:08 Moonshademyth Vacation With NF and Wages
So this is my first nanny job EVER. I have tons of kid experience, I love this family, and MB wants me to go with her on vacation with her kids. This will happen often it seems, little trips 2-3 days. She said my room and travel will be paid for by her, but should I be expecting wages too? Should I ask about wages? I have bills to pay and can't really afford to just go without the hours to still work. Does that make sense?
Do you guys get wages for trips?
submitted by Moonshademyth to Nanny [link] [comments]
2021.10.20 00:08 PixellatedPirates 🇺🇸Joe Biden Pixel☠️Pirate - Pixellated Pirates Collection!
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2021.10.20 00:08 TooZeroLeft What's your favorite non-main planet in Star Wars? Mine has to be Mygeeto. It looks unique and atmospheric for the brief moments we see it
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2021.10.20 00:08 Glittering-Watch-164 nipples
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2021.10.20 00:08 alsetkin Live council meeting employee vaccine mandate
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2021.10.20 00:08 Ciao15 why are black women named asia?
I don’t mean this at all in a racist way, but... as an asian person I do scratch my head when i see non-asian women named Asia. Asian women wouldn’t be named Asia, so i guess it’s always a non-asian women, and so far it has been black women I noticed who have the name. I would imagine it would be quite strange to see an asian woman named Africa, right? Is there any meaning for this or back story/origin...? just curious.
submitted by Ciao15 to Names [link] [comments]
2021.10.20 00:08 Fun_Significance5984 calling my self out
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2021.10.20 00:08 PIZT Anyone else try drinking Kombucha for gout?
2021.10.20 00:08 Other-Education5840 Been Censored Off r/Funny For "Reposts" When I Didn't Repost
I notice the mods on here are power hungry and have nothing going in there lives than to attempt to make others miserable. Misery loves company! It's ironic because I was sharing funny memes and content designed to make people feel good for the day and destress then here comes a Power-trip mod to censor and remove you from their subreddit.
Reddit is shit and should honestly be disbanded entirely if they continue with the constant censorship and draconian rules they make up on the fly !
Keep being a positive influence in people and keep outshining the keyboard warriors ! 🤴
submitted by Other-Education5840 to BadMods [link] [comments]
2021.10.20 00:08 mariosquarepants OBAMIUM
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2021.10.20 00:08 descartes_robocop Someone had PC problems at work. I went to check how long its been on.
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2021.10.20 00:08 don_rampanelli eu_nvr
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2021.10.20 00:08 chikkinnveggeeze Pixel 6 financing
2021.10.20 00:08 missionlake2 Wow
|submitted by missionlake2 to LissandraMains [link] [comments]|
2021.10.20 00:08 moktira Terrible PlosOne Paper Dissected
The paper is entitled "The anti-vaccination infodemic on social media: A behavioral analysis" and can be read here. The idea is to compare the behaviour of Twitter users who are pro- and anti-vaccine and the results claim that Trump "was the main driver of vaccine misinformation on Twitter" which is something I saw in the media and would have naively believed until I read this anti-scientific flawed-statistical work.
Sadly, nobody who reported it seems to have read it either, I initially came across it on science earlier in the year (here) but only recently got around to reading it, there you can see most commenters also didn't read it, just comment on the results reported in a news article on it, the highest rated comment claims "this is supported by network theory." Unfortunately it is not supported by the shambolic network science in this paper (see Network Analysis section below). I will go through many of the flaws but do not have the time or patience to list them all!
The study attempts to compare Twitter users who support and oppose vaccines in response to COVID-19. To do this they take 50 users who used the hashtag #vaccineswork and 50 who use #vaccineskill and #vaccinesharm. They get a control group of 50 users by searching words from a random word generator and call this #control.
Issues: Firstly, there are around 1 billion twitter users, choosing 50 is not a representative population, how are these chosen? Just the first 50 when they searched for that hashtag? A random selection of 50 who use that hashtag? Unknown as they don't describe this. Secondly, it seems like an issue that there are twice as many search terms for anti-vaccination users as vaccination users and it's not clear if they had to use both or one or the other. Thirdly, using a random word generator is a bad idea as not only could it just be nonsense, but you could also pick something related to the two topics. In order to even do this properly you should replicate it lots of times and take an average of your results, of course they don't do this...
From this tiny sample they discover anti-vaccination users are more likely to retweet, pro-vaccination people are more likely to reply, something you can only claim about this sub-sample, not the population which is what they do. Next they "quantified the number of conspiracy theory (CT)-associated contents (tweets and retweets), as well as the number of emotional contents (either depicting emotional situations or adopting emotional language) shared by control, anti-vaccination and pro-vaccination profiles." How do they define emotional tweets? They don't other than what's in the parenthesis so it could be entirely subjective, and they don't mention it further in the supplementary material. So this is not described and as their sample is not properly described, this is entirely unreplicable.
They next look at the most common words used by each group and shockingly find: "As expected, the word “vaccine(s)” was the most represented in both groups, confirming that our initial criteria for inclusion were reasonable." No, this doesn't confirm your inclusion was reasonable, it confirms that searching for a hashtag with the string "vaccine" in it, did in fact find Tweets with the term "vaccine" in it. So Twitter's search is not broken is all this confirms.
They next check whether use of emotional (still not clearly defined) language is related to increased engagement (sum of number of replies, likes and retweets per tweet) and produce this gem:
If my first year undergrads showed me something like this I would not be happy!
We see here that one single outlier drives a poor correlation on the right and from this they conclude that for the pro-vaccination users there is a "significant correlation between the two aforementioned factors (Fig 3D’), suggesting that the use of emotional language could aid the success of the pro-vaccination communication strategy online." There is unfortunately no way to believe this claim, again: emotional language is undefined, and one single outlier is driving this very low correlation.
They next look at the profiles being retweeted by 42 of the anti-vaccination and pro-vaccination users (not sure why this is reduced from 50), they choose the 10 most retweeted profiles (presumably of each user) and create a network. Technically this is a directed network, as just because they retweet someone does not mean that person even follows them, so the network measures chosen should reflect that (note: they don't), and obviously it's not complete as they choose only 10 profiles retweeted rather than all. Here is the network measures they show:
Worst network measures figure ever?
No conclusions can be drawn from this, but let's go through it. Earlier they mentioned that anti-vaccine tweeters retweet more. They fail to mention here that most of their 42 users do not retweet 10 accounts, so the first quantity, number of neighbours is lower for those who retweet less, what does this mean? Pretty much nothing, that those who retweet less, retweet less, what do they claim it means: "that anti-vaccination supporters are well-connected in a community". They also showed earlier that pro-vaccine users reply more, so why not look at the reply network too? What if that got opposite results that showed they're in a well connected community? Surely a reply network is more indicative of community than a retweet network? We have no idea, because they didn't bother to do it.
[Further network measures: The second quantity here is the clustering coefficient, think of this quantity as follows, if there is a high clustering coefficient, for two people you know there is a high probability they know each other (this is related to the number of triangles in the network). As the scale is potentially log at this stage and the symbols are large these values are indecipherable (to be fair they do report them in the paper but this visualisation is terrible). As there are less links in the pro-vaccine people, they have a smaller clustering coefficient. This could just mean that pro-vaccine people retweet different people, more likely it means they have have a poor sample, which we know they do. The density is low, this means there is a small number of actual links compared to number of possible links (again meaningless -- the networks are sparse is what it means but that's usually the case). Finally they show the average path length, this tends to roughly scale with the log of network size which is what they show. So what does this whole section tell us? Basically nothing, they have networks that are slightly different and how not to represent network quantities.]
They next introduce an edge cut-off (why? unknown) and show the most retweeted people in their biased sample of a network:
Love D! Word cloud with one entry!
And here we see their conclusion, Trump is the cause of it all. The really infuriating thing here is, had they done the study properly they might have found this and it would be interesting (I think maybe someone else has since). But because they choose 42 (possibly random) users (from December 2020) with the hashtags #vaccinesharm and (or?) #vaccineskill, take their top 10 retweets, cut-off anyone with less than 5 connections, we have no idea if they just picked 42 Trump supporters, or if most anti-vaccination people out there in the world are actually influenced by Trump. Because they only look at retweets, and not followers or replies, we have no idea what the 42 pro-vaccine people are actually doing to properly compare. Because they only chose one sample of 42 of each, we have no idea if this is a statistical anomaly or the norm. Due to their poor description of their data gathering and lack of description of terms, this is impossible to replicate. And due to their statistically insignificant samples, shambolic statistics and flawed network analysis, none of their conclusions can be taken seriously.
How did this get published?
So the obvious next question is, if this is so terrible, how did it get through peer review? People often have this notion of peer review being some gold standard in science but sadly, it's a bit of a lottery. PlosOne do however, give the option to show the peer reviews after and luckily, these guys accepted that, so you can read those here. Basically the first says "put in the following references" (the cynically minded might assume that some of those are papers by that reviewer to increase their citation count), and the second says "Very interesting, you should replace all instances of 'President Trump' with 'former President Trump'". And that's it! So clearly neither reviewer actually read it in any detail.
I blame the editor here too, they should look at it to know it's poor quality, PlosOne prides itself on aiming to have valid science even if this yields no results. This paper provides results with invalid science, the editor should quickly be able to identify this, and should be able to tell at least one reviewer did not read the paper.
I think I'll stop there, the discussion in the paper has more unsubstantiated nonsense and there are plenty of further flaws you'll find if you even skim the paper. I feel I've given too much time to this atrocity as is so need to do something useful with my time now!!
submitted by moktira to badscience [link] [comments]
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submitted by Previous-Maximum1764 to CryptoMars [link] [comments]
2021.10.20 00:08 OneKOff Warlock to an Aggro Deck: "I've suffered long enough! It's YOUR turn now!"
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2021.10.20 00:08 Pumuckl4Life Haiti: Lösegeldforderung nach Entführung von Missionaren
|submitted by Pumuckl4Life to USA_de [link] [comments]|