Yeah, again you're living in an ideal world. The reality is librarians receive
requests from their customers for subscriptions to journals. So librarians
might receive requests to subscribe to crap journals (requests made, perhaps,
by students or new lecturers that haven't done their homework) and they're
going to have to decide whether the journal is crap, assuming they have the
budget to work with in the first place.
Just saying "if it's crap, don't buy a subscription" is naive to the point of
uselessness. It's like those Nigerian scam emails. They wouldn't keep doing it if the hit
rate were zero. And, yeah, you can always claim that 95 year old woman struggling to pay
for her cancer treatment *should* get scammed because she's stupid. But, in reality, most
people won't make silly claims like that. Those of us who *can* help stop scammers,
should help stop scammers.
On 9/8/22 06:57, Marcus Daniels wrote:
If a journal is crap, don't buy a subscription for it. Mostly I think there
is a lot of publication that doesn't need to occur, and the incentives for that
are largely to blame.
-----Original Message-----
From: Friam <friam-boun...@redfish.com> On Behalf Of glen
Sent: Thursday, September 8, 2022 5:54 AM
To: friam@redfish.com
Subject: Re: [FRIAM] Still more faking it till you make it
Well, there are analogs to "irate customer generating negative feedback" in polluting the
commons. The real difference between the rhetoric in that optimal fraud article and things like
AI-generated nonsense publications is the "waterfall" accountability. There's an
equivalent waterfall accountability to processing information from ill- or non-curated sources. But
it's not measured as well, nor are the costs proportionally born by those involved.
Marcus' suggestion that all the cost should be (is) born at the edge is pure fantasy. The costs
are also born by, e.g. every editor with a shred of integrity, every institution that pays for
subscriptions, every researcher hunting for a "good" place to submit their work, etc.
More banal examples might be wellness channels on Youtube, places like Goop
<https://goop.com/>, or the nutriceutical market(s). A poignant example is the Log4Shell
supply chain vulnerability.
Garbage can be inserted into any branch point in the (pollutable) supply chain. And the
costs are born by the entire chain, proportionality depending on whether it's
"regulated" by conscious attendees (like the head of Fraud in Business, Inc. or
the actuaries at the insurance companies).
On 9/7/22 20:02, Roger Critchlow wrote:
In a similar vein, this article showed up on hackernews last week
https://bam.kalzumeus.com/archive/optimal-amount-of-fraud/
<https://bam.kalzumeus.com/archive/optimal-amount-of-fraud/>
though a fraudulent credit card transaction has an irate customer generating
negative feedback, where a badly edited journal merely pollutes the commons.
-- rec --
On Wed, Sep 7, 2022 at 9:42 PM Marcus Daniels <mar...@snoutfarm.com
<mailto:mar...@snoutfarm.com>> wrote:
What is the problem with paper mills? Cite papers that are important,
ignore papers that are not.
On Sep 7, 2022, at 1:32 PM, glen <geprope...@gmail.com
<mailto:geprope...@gmail.com>> wrote:
We need to talk about editors
http://deevybee.blogspot.com/2022/09/we-need-to-talk-about-editors.ht
ml?m=1
<http://deevybee.blogspot.com/2022/09/we-need-to-talk-about-editors.h
tml?m=1>
I know. Ya'll are prolly unsubbing because of all my spam. But until
Voldemort bans me, this is the state of the world.
What does it say about me that I find this beautiful:
"Asthma disease are the scatters, gives that influence the lungs, the
organs that let us to inhale and it’s the principal visit disease overall
particularly in India. During this work, the matter of lung maladies simply like the
trouble experienced while arranging the sickness in radiography are frequently
illuminated. There are various procedures found in writing for recognition of asthma
infection identification. A few agents have contributed their realities for Asthma
illness expectation. The need for distinguishing asthma illness at a beginning
period is very fundamental and is an exuberant research territory inside the field
of clinical picture preparing. For this, we’ve survey numerous relapse models,
k-implies bunching, various leveled calculation, characterizations and profound
learning methods to search out best classifier for lung illness identification.
These papers generally settlement about winning carcinoma discovery methods that are
reachable inside the
writing."
--
ꙮ Mɥǝu ǝlǝdɥɐuʇs ɟᴉƃɥʇ' ʇɥǝ ƃɹɐss snɟɟǝɹs˙ ꙮ
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