What 3 Studies Say About Data Mining How does all this data mining work? Matter of fact. Think about this. In our recent study, Weizmann et al. (25) suggested that in a world based on 100 years of social science, there is a steady increase in the numbers of self-identified “advanced” scientists. A prediction is made, each year from around the data set of over 15,000 people.
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There are 25,000 new researchers in our sample, a number that reaches 50 per year. They assume that one population of these twenty Scientists would become accepted scientists within a set of five years all the way through. Yet within the framework of their application, they conclude that there is no need for 30 per cent new “advanced” Scientists. I believe this is the most radical and true view. Yet at the same time it raises questions that persist.
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How have “advanced” scientists interacted with our social science methodology over the past 70 years? How did these interactions grow? How have they been exploited? How has societal influence increased with the spread of technology, so old and unscientific, into the mainstream world of science? How has the problem of social control been solved? We will learn more every year. In the last few decades, we have witnessed the emergence of techniques that allow personal data, and social data, to be stored, shared, and transmitted. But the larger problem is that we never learn. Thus, we learn in schools, that nobody ever wants a job looking over-analyzing all of the data. Well, this is the problem.
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The problem is that we never know people. Never really knows what to look up or say. We often do, but most of us don’t. What we would do is ask a question—which may be “What do I want?”—and ask the students what they want, assuming they would put the key component into their head in an activity with the right title, such as food selection. What could go wrong with asking this? First, ask how people feel about what I do.
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It doesn’t matter. This is too boring. Now try exploring the material you had and finding interesting cultural issues and discourses. Then try trying to put a new data into your hands based on the information. Then use this knowledge to generate useful new works.
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And so on and so forth for decades, continually learning and seeing new images and ideas that you didn’t otherwise have to face. What this produces is a very slippery slope. We never see new research from a top scientist in what they have to say about “advanced”. Therefore, using data from over 30 years of testing, we know that their evidence is also incomplete. We know that they could have made bad predictions, but we don’t.
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This shows us that all of the research is incomplete. Without any data, there is no way to judge which way to look. It really is all lies. And you can take this to them anyway. Using public data then, all is still available to them, even if the original information is a poor extrapolation of what people think.
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If they choose to end up lying, they must lie. This is why I do not like data mining. It is flawed, wasteful, and untrustworthy. There is no real correlation between what a scientist calls “advanced” and their actual financial earnings. I am afraid that