So here's something interesting about our latest 'surge'. If one goes to https://t.co/unPSmmc8Mg you can toggle between 'confirmed' cases, 'probable' cases and both of them together.

So what is a 'confirmed' case? That would be any laboratory confirmed 'case' - any positive PCR, antigen or antibody test, with all the issues of false positivity and hypersensitivity and non-infectiousness that go along with all of those tests.
A 'probable' case does not even have a positive test result associated with it. All it requires are symptoms. Maybe an epidemiological link. We are now in the middle of what used to be referred to as 'flu season' when there is a wide variety of respiratory illnesses that exist -
- and are passed around, creating symptoms in individuals that count under one of the 44 symptoms of 'COVID' in Ohio.
⭐️⭐️⭐️ So how big of a deal are the probable cases in Ohio? On the left are just the 'probable' cases/hospitalizations/deaths. On the right are the total assigned cases/hospitalizations/deaths -
the numbers that are reported daily and are being used to continue to increase anxiety and destroy lives. I have labeled the peaks of the probable data, and labeled the corresponding day for the total side.
⬇️⬇️⬇️⬇️⬇️⬇️
19.7% of January 4th's 'cases' were probable.

8.7% of December 8th's hospitalizations were probable.

26.9% of December 13th's deaths were probable.
⬇️⬇️⬇️⬇️⬇️⬇️⬇️
Nearly 10% of ALL deaths are considered 'probable' - meaning there was no laboratory confirmation that the deceased even had the virus when they died or during their life at any point.
The comparison of the cases graph is also interesting. Notice that the total cases rose and fell from the end of October through mid-December, just as all of our data shows. But then we have had a more recent 'spike.'
On the probable side of things, there was no wave visible in November/December. Instead we see a steady increase in cases that are being considered probable. That is, cases that DO NOT have an associated positive test result of any sort.
⭐️⬇️⭐️⬇️⭐️⬇️
Nearly 1/5th of our newest 'peak' cases have NO positive test result associated with them.

They are just based off symptoms. In the middle of flu season.
#InThisTogetherOhio

More from Society

This is a piece I've been thinking about for a long time. One of the most dominant policy ideas in Washington is that policy should, always and everywhere, move parents into paid labor. But what if that's wrong?

My reporting here convinced me that there's no large effect in either direction on labor force participation from child allowances. Canada has a bigger one than either Romney or Biden are considering, and more labor force participation among women.

But what if that wasn't true?

Forcing parents into low-wage, often exploitative, jobs by threatening them and their children with poverty may be counted as a success by some policymakers, but it’s a sign of a society that doesn’t value the most essential forms of labor.

The problem is in the very language we use. If I left my job as a New York Times columnist to care for my 2-year-old son, I’d be described as leaving the labor force. But as much as I adore him, there is no doubt I’d be working harder. I wouldn't have stopped working!

I tried to render conservative objections here fairly. I appreciate that @swinshi talked with me, and I'm sorry I couldn't include everything he said. I'll say I believe I used his strongest arguments, not more speculative ones, in the piece.

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महाभारत की कहानी कौन नहीं जानता।लेकिन क्या आपको पता है कि महाभारत के ज्यादातर पात्र किसी न किसी श्राप में फंसे थे।अगर ये श्राप न होते तो कदाचित महाभारत की कहानी कुछ और होती।हिन्दु पौराणिक ग्रंथों में विभिन्न श्रापों का वर्णन मिलता है व हर श्राप के पीछे कोई कहानी अवश्य होती है।


आइए आज जानते हैं महाभारत कथा में वर्णित कुछ श्रापों के बारे में।

1) राजा पाण्डु को ऋषि किन्दम का श्राप

एकबार महाराज पाण्डु शिकार खेलने वन गए।झाडियों के पीछे कुछ हिल रहा था। मृग है सोचकर राजा ने बाण चलाया जो जाकर ऋषि किन्दम और उनकी पत्नी को लगा।वे दोनो रति-क्रीड़ा में लिप्त थे।

जब राजा ने उन्हें देखा तो बहुत दुखी हुए कि ये मुझसे क्या पाप हो गया।बहुत क्षमा याचना के बाद भी किन्दम ऋषि ने पाण्डु को श्राप दे दिया कि जब भी वो किसी स्त्री को काम भावना से स्पर्श करेंगे उसी क्षण उनकी मृत्यु हो जाएगी।पश्चाताप करने, वे सिंहासन पे अन्धे राजा धृतराष्ट्र को बैठाकर...


..स्वयं अपनी रानियों कुंती व माद्री के साथ वन चले गए।पांडवों का जन्म भी कुंती को ऋषि दुर्वासा द्वारा दिए गए मंत्र से हुआ था जिसमे किसी भी देव का स्मरण कर उस देव से कुंती,पुत्र प्राप्त कर सकती थी।एक बार माद्री पे मोहित हो जब पांडु ने उसे स्पर्श किया,उसी क्षण पांडु की मृत्यु होगयी।


2) उर्वशी का अर्जुन को श्राप

महाभारत युद्ध से पहले जब अर्जुन दिव्यास्त्र प्राप्त करने स्वर्ग गए तो वहां उर्वशी नाम की अप्सरा उन पर मोहित हो गयी। अर्जुन ने जब उन्हें अपनी माता के समान बताया तो यह सुनकर उर्वशी क्रोधित हो गयी और अर्जुन को श्राप दे डाला कि तुम नपुंसक की भांति...
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.