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Joined 28 days ago
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Cake day: February 14th, 2025

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  • This is a super unpopular opinion in 2025, but I’m a grown up and happy to take the downvotes.

    Jury Nullification isn’t really a “thing” as in it’s not intended to be a function available to the jury.

    The justice system intends for Jury’s to perform a very specific function: to find a defendant guilty or not guilty beyond reasonable doubt.

    However, jurors must be able to make that determination free from any concerns as to repercussions against them. The system couldn’t work if a juror feared being held responsible for their finding. Imagine if overlooking or misinterpreting something as a juror could be a crime? It would present a very ready mechanism for corruption “Any juror that finds Trump guilty will be subject to prosecution by the next republican government”.

    So, jurors have absolute protection from any responsibility as to their findings, and as such they are able to say “we think luigi probably did commit this crime but he seems like a great guy so our unanimous finding is not-guilty”.

    It’s a subversion of the justice system. Jurors may take this third option without consequence but they are not upholding their responsibilities to the justice system.

    My concern with jury nullification is that if jurors can decide whether the law should apply in whatever case, they’re essentially making up the law based on nothing more than their feelings about what happened. Additionally, it makes a court case more of a popularity contest than a fair application of the law.

    The common rebuttal to what I’ve said is that the justice system is rarely just. That may be the case but justice is not going to be improved by moving to a kangaroo court. We may as well throw defendants in the river and pronounce those who do not drown to be guilty.






  • There’s a variety of reasons.

    1. instances are communities, some more loose than others
    2. some instances have themes. lemmy.world is general, aussie.zone welcomes anyone but it’s mostly Australians or friends there-of. Having a user name/address like vvilld@aussie.zone says something about you.
    3. some instances have ideological alignments, lemmy.ml are tankies
    4. some instances de-federate from others, like hexbear is not federated by some / many instances.
    5. the “local” feed can be like a set of subscriptions or curated content. slrpnk.net is the best example I can think of for this, mostly environmentally conscious tech, and renewables, et cetera.
    6. some admins turn out to be idiots. In the early days there was some support from admins of specific / niche instances for bots re-posting content from reddit.
    7. there’s no good reason not to have multiple accounts. In my own case I change instances regularly. IDK why exactly, I just actively avoid allowing my account to become some kind of extension of my identity.


  • this is a silly hill to die on

    indeed

    What filters are these? I’ll have to keep an eye out for the grammar section in the inbound spam/phishing policies next time I’m managing a client in the exchange section of their tenant. Bad luck for those who don’t spell well, can’t use spell check or are ESL, I guess. Mistyped URLs or domains however, sure are a thing.

    I can’t believe I need to explain this to Mr exchange server administrator, but you have it the wrong way around. Spelling errors are a common strategy to avoid emails being classified as spam. Bayesian filters collate tables of words that commonly appear in spam. Spelling errors create words that the filter hasn’t seen classified as spam.


  • a huge number of spelling mistakes screams spam to me

    Do you mean to say, you’ve learned to associate spelling errors with spam because most of the spam you see… the spam that gets past your spam filters… has a lot of spelling errors?

    The best way to deliver spam is to make it indistinguishable from legit messages.

    That’s just not true. The best way to deliver spam is to send it from a reputable address, and to avoid looking like spam.

    Bayesian filters need to be trained by a user identifying email as spam. From those emails it learns which words frequently appear in spam emails. Including spelling errors means more unique words rather than words that look like spam.