Thread on Justice A.K.Rajan Committee report.
Issues
1.Selective usage of Data.
2. Makes several unverifiable assumptions
3.Non exclusion of outlier data
4.Drawing inferences/conclusions without evidence

Concerns
1. Data on students from Govt schools who got MBBS seats pre and post #NEET is selectively put out.(Everything else is from 2010-2020. This alone is from 2014) . Why?
2. How did Committee decide that 99% of students who qualified through NEET took coaching?
3.Only students from rural areas will go and serve in rural areas.Those from urban areas will not is an assumption here. Evidence for this?
4.2017 the 1st year of NEET is an outlier. All the data that year was way off from subsequent years.Was this data normalized?
5. How does one decide that 90% in State Board is better than 60th percentile of NEET? The report indicates so.
6.NEET will spoil the quality of medical education and healthcare system in Tamil Nadu says the report. There is no reasoning how medical education will be damaged.
And the report says doctors will not be available at all in future in TN. How did they arrive at this conclusion?
7.CBSE school students got 0.11% pof seats in Govt medical colleges before NEET. Was this social justice?8. Govt school students got 1% of seats before NEET which
dropped further with NEET until the 7.5% reservation came. But was 1% social justice in the first place?
9. Rural students who comprised 52% of the population and an even smaller % of the applicants were getting 60%+ seats before NEET. Now it has come to 50% which is in line
with population and still favours rural in terms of applicants. Why is that injustice?
10. Among OC category,FC students got more seats under NEET is one argument.However this too normalized to pre NEET levels after the 1st 2 years. Also isn't OC meant to be open competition.
How can one claim quotas in this 31%.What was the purpose of compiling this data except to suit the narrative?
11.More students opting out of Science Group in State Board is bcos of NEET. How did they conclude this?
12. Children of parents who earn more have a better chance now
Since there is no big difference among social catgeories in this does the report suggest creamy layer exclusion (a concept rejected by TN Govt.). Most welcome if this is the case
13. Tamil medium students have been affected big time. This has been addressed.
14.Why is the trend between 2017-18 and 2020-21 not given any importance at all? Many of the parameters seem to be heading towards pre NEET days but the committee does not even mention this. eg.FC as % of OC from 23% to 11%,
15. More students from TN have been clearing NEET
with each passing year. This does not even get a mention. Why no data on this?
16. How many students took coaching in Govt NEET coaching Centres?How many got seats through this coaching?Why is there no mention of this? Is this not important when pvt.Coaching centres are mentioned
in so much detail?
17. Why is repeating an exam projected as a bad thing? Earlier you had one shot at getting a seat. Now you have 3 chances. Only those who wish to spend a year studying and repeating an exam do so. This happens in most competitive exams. Why is that wrong?
If this is because of them being able to afford coaching fees then why not Govt give free coaching?
18.Nowhere does the report even speak of Govt coaching Centres for NEET. Is that not a solution to help disadvantaged students?Instead the focus is on how much fees private centres
collect and 5700+ crore industry etc.
19. HDI Districts get more seats after NEET than more backward Districts say the report. Yet only 5 or 6 Districts are selectively quoted. And pre NEET vs Post NEET data is not given for all Districts. Why?
20. The most important
conclusion that trends are returning to pre NEET levels in most areas (eg.Parental income levels, rural vs urban, OC vs BC vs MBC etc.) has been completely ignored.
Would be glad to see any constructive criticism of the points raised. No abuse please. Read 165 pages overnight
and with some fatigue too. If I have missed something please point it out and I will be glad to stand corrected. #AKRajanReport #Akrajancommittee

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How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

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By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

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