Einstein once said: "If a person is forced to write a large number of scientific works, his academic career will risk becoming superficial."

I have met many talented researchers in various academic forums. They have very sharp thinking and logic. During conversations, they can follow my train of thought and give me some very insightful suggestions.

But when I got back, I looked through their papers, and most of them, even all of them, were meaningless, just boring piles of numbers.

Their papers and they themselves left me with completely opposite impressions.

Similar accusations have emerged repeatedly over the years, although the academic community has consistently defended these criticisms, claiming:

Research in basic sciences is difficult and gradual, much like the blood transfusion used in nuclear magnetic resonance imaging (MRI) and the blood transfusion method mentioned in a paper published in an obscure academic journal in the 1800s.

We need a lot of repeated experiments to derive sufficiently valuable results.

These seemingly useless results are what pave the way for truly useful ones.

But is it really necessary for PhDs or even professors to do these piled-up useless results, which are just fine-tuning the method and repeating the data over and over again in order to get the desired data?

Currently, many jobs in the manufacturing and service industries are being replaced by artificial intelligence. Could academia provide some low-end positions for ordinary people to allow more people to participate in academia?

The science city fully respects the individual and is not paper-oriented or results-oriented. I believe it is a place conducive to scientists thinking about truly valuable questions.

In fact, researchers are paper-oriented; all researchers pursue publishing papers, rather than focusing on what questions they are interested in, wanting to explore the truth, or wanting to push the boundaries of their understanding of the world further.

The fundamental reason is that the academic community needs a mechanism to evaluate the performance of professors, researchers, and doctoral students.

If science is a towering tree, then the lower fruits have been mostly picked by our predecessors, and what remains are mostly fruits that are very difficult to pick.

For example, it might only take me three to five years to come up with a solution to a valuable problem, which is exactly the cycle of a doctoral student from enrollment to graduation.

In these five years, I have been able to produce a valuable result, publish a meaningful paper, and graduate smoothly.

But now I also have about five years to publish my doctoral dissertation, but the fruits that I could pick in five years are gone, and other fruits may take fifteen years or even longer to pick.

At this point, they also faced the pressure of graduating.

Then I can only choose to do some results that are meaningless but can be published in papers.

After graduating with a PhD, one goes on to become a postdoctoral researcher or a teaching assistant; almost every title comes with performance evaluations and time pressure.

This is why it seems increasingly difficult for academia to produce good results, with a large number of meaningless papers flooding various journals.

The difficulty of scientific research has changed, but the evaluation mechanism has remained unchanged.

This problem is not unique to China; it is a challenge faced by the global academic community.

The only advantage compared to China is that in foreign academic circles, once graduates enter universities, they become part of the TERACK evaluation process, and the pressure of this performance evaluation is not very high.

Furthermore, once you complete the course, you will indeed be able to obtain a permanent position and become a tenured professor.

Not only is the performance appraisal pressure in China immense, but the agreed-upon assessment tasks may also be changed suddenly because the young teachers recruited in later years are more outstanding.

There are two positions available overseas, so we'll hire two people and develop a terack assessment plan.

There are two positions available domestically, so I'll hire two hundred people. Let these two hundred people compete fiercely, and the winner might be replaced later.

In such a nurturing environment, it would be strange if data manipulation and a sole focus on academic papers were not employed.

If you spend your time tinkering with valuable questions, then just wait three years without producing results and then graduate.

After the science city officially started operating, the largest number of applicants were from China.

The assessment mechanism of the Science City is incredibly easy for those who have fought their way out of the vicious cycle.

Moreover, for scientists in China, once you become a professor, you can hold positions in multiple places simultaneously.

For example, dybvig, the former dean of the School of Economics at Southwestern University of Finance and Economics, won the Nobel Prize in Economics in 2022. When he was working at Southwestern University of Finance and Economics, he only came to the university for one month each year.

The constraints on the science city are very weak, almost non-existent.

They can work in their home country and come to Singapore whenever they want.

Being a researcher in the science city is essentially an entry qualification; once obtained, one can enter the science city at any time and enjoy corresponding benefits.

Zheng Li doesn't care where you produced your work.

Whether you produce good and valuable research results in China, America, or England, the Science City will recognize them.

Of course, the research results here need to be done by you personally, and your personal contribution should be at least 50%.

Since the final verification of the results needs to be conducted back in the Science City, Zheng Li had already provided a truth system for China to use more than ten years ago, let alone in his own backyard.

Trying to steal someone else's academic achievements through deception and then try to fool artificial intelligence is wishful thinking.

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