[list][*]Which of the following best describes the phrase critical mass as used in the passage ?[/list] ?->(Show Answer!)

1. [list][*]Which of the following best describes the phrase critical mass as used in the passage ?[/list]

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MCQ-> Our propensity to look out for regularities, and to impose laws upon nature, leads to the psychological phenomenon of dogmatic thinking or, more generally, dogmatic behaviour: we expect regularities everywhere and attempt to find them even where there are none; events which do not yield to these attempts we are inclined to treat as a kind of `background noise’; and we stick to our expectations even when they are inadequate and we ought to accept defeat. This dogmatism is to some extent necessary. It is demanded by a situation which can only be dealt with by forcing our conjectures upon the world. Moreover, this dogmatism allows us to approach a good theory in stages, by way of approximations: if we accept defeat too easily, we may prevent ourselves from finding that we were very nearly right.It is clear that this dogmatic attitude; which makes us stick to our first impressions, is indicative of a strong belief; while a critical attitude, which is ready to modify its tenets, which admits doubt and demands tests, is indicative of a weaker belief. Now according to Hume’s theory, and to the popular theory, the strength of a belief should be a product of repetition; thus it should always grow with experience, and always be greater in less primitive persons. But dogmatic thinking, an uncontrolled wish to impose regularities, a manifest pleasure in rites and in repetition as such, is characteristic of primitives and children; and increasing experience and maturity sometimes create an attitude of caution and criticism rather than of dogmatism.My logical criticism of Hume’s psychological theory, and the considerations connected with it, may seem a little removed from the field of the philosophy of science. But the distinction between dogmatic and critical thinking, or the dogmatic and the critical attitude, brings us right back to our central problem. For the dogmatic attitude is clearly related to the tendency to verify our laws and schemata by seeking to apply them and to confirm them, even to the point of neglecting refutations, whereas the critical attitude is one of readiness to change them - to test them; to refute them; to falsify them, if possible. This suggests that we may identify the critical attitude with the scientific attitude, and the dogmatic attitude with the one which we have described as pseudo-scientific. It further suggests that genetically speaking the pseudo-scientific attitude is more primitive than, and prior to, the scientific attitude: that it is a pre-scientific attitude. And this primitivity or priority also has its logical aspect. For the critical attitude is not so much opposed to the dogmatic attitude as super-imposed upon it: criticism must be directed against existing and influential beliefs in need of critical revision – in other words, dogmatic beliefs. A critical attitude needs for its raw material, as it were, theories or beliefs which are held more or less dogmatically.Thus, science must begin with myths, and with the criticism of myths; neither with the collection of observations, nor with the invention of experiments, but with the critical discussion of myths, and of magical techniques and practices. The scientific tradition is distinguished from the pre-scientific tradition in having two layers. Like the latter, it passes on its theories; but it also passes on a critical attitude towards them. The theories are passed on, not as dogmas, but rather with the challenge to discuss them and improve upon them.The critical attitude, the tradition of free discussion of theories with the aim of discovering their weak spots so that they may be improved upon, is the attitude of reasonableness, of rationality. From the point of view here developed, all laws, all theories, remain essentially tentative, or conjectural, or hypothetical, even when we feel unable to doubt them any longer. Before a theory has been refuted we can never know in what way it may have to be modified.In the context of science, according to the passage, the interaction of dogmatic beliefs and critical attitude can be best described as:
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MCQ->[list][*]Which of the following best describes the phrase critical mass as used in the passage ?[/list]....

MCQ-> Analyse the following passage and provide appropriate answers for the questions that follow: Each piece, or part, of the whole of nature is always merely an approximation to the complete truth, or the complete truth so far as we know it. In fact, everything we know is only some kind of approximation, because we know that we do not know all the laws as yet. Therefore, things must be learned only to be unlearned again or, more likely, to be corrected. The principal of science, the definition, almost, is the following: The test of all knowledge is experiment. Experiment is the sole judge of scientific “truth.” But what is the source of knowledge? Where do the laws that are to be tested come from? Experiment, itself, helps to produce these laws, in the sense that it gives us hints. But also needed is imagination to create from these laws, in the sense that it gives us hints. But also needed is imagination to create from these hints the great generalizations – to guess at the wonderful, simple, but very strange patterns beneath them all, and then to experiment to check again whether we have made the right guess. This imagining process is so difficult that there is a division of labour in physics: there are theoretical physicists who imagine, deduce, and guess at new laws, but do not experiment; and then there are experimental physicists who experiment, imagine, deduce, and guess. We said that the laws of nature are approximate: that we first find the “wrong” ones, and then we find the “right” ones. Now, how can an experiment be “wrong”? First, in a trivial way: the apparatus can be faulty and you did not notice. But these things are easily fixed and checked back and forth. So without snatching at such minor things, how can the results of an experiment be wrong? Only by being inaccurate. For example, the mass of an object never seems to change; a spinning top has the same weight as a still one. So a “law” was invented: mass is constant, independent of speed. That “law” is now found to be incorrect. Mass is found is to increase with velocity, but appreciable increase requires velocities near that of light. A true law is: if an object moves with a speed of less than one hundred miles a second the mass is constant to within one part in a million. In some such approximate form this is a correct law. So in practice one might think that the new law makes no significant difference. Well, yes and no. For ordinary speeds we can certainly forget it and use the simple constant mass law as a good approximation. But for high speeds we are wrong, and the higher the speed, the wrong we are. Finally, and most interesting, philosophically we are completely wrong with the approximate law. Our entire picture of the world has to be altered even though the mass changes only by a little bit. This is a very peculiar thing about the philosophy, or the ideas, behind the laws. Even a very small effect sometimes requires profound changes to our ideas.Which of the following options is DEFINITLY NOT an approximation to the complete truth?
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MCQ-> Read the following passage carefully and answer these question. Certain words/phrase have been printed in bold to help you locate them while answering some of the question.Over the last three centuries, the world economy has evolved from a predominantly agriculture-based system to a digital economic system. The earlier economies were mainly agrarian. In this era, capital did play a role, as did technological innovations such as the plough, the steamboat or the train. But land and labour were more critical.With the industrial revolution, the global economy was primarily driven by the ability to produce goods for the mass market. This led to the industrial economy where capital and labour were the most important drivers. In the service economy, the wealth created by services exceeded the wealth created through manufacturing. Here, the ability of the service provider to establish a sound business gave him access to additional capital. This evolved into a global economy where goods and services were traded across international borders, with little restriction. ln this period, capital started flowing across border on all large scale for the first time.The last five years have seen the advent of the digital economy where technology is becoming the driving force. With information being the driver of value and wealth creation, information logy is becoming the key to success in a growing number of industries. In the digital economy, the power of innovation and ideas gained the upper hand over direct access to capital.The Indian economy is in a unique in terms of its economic evaluation. While manufacturing and service industries in India cannot freely access capital, the new breed of IT:- based industries have access to venture capital and private equity. The country's potential in this emerging sector has opened the doors to capital inflows that are still not available to traditional industries.There are two key trends which will boost the democratization of capital, either directly as funding sources or indirectly.More effective capital market routes---especially for information - based and software companies.This is already happening rapidly. A market that was supposed to be stagnating with no public offering from the manufacturing sector in the first quarter of the fiscal year may see as many see as many as 20-25 new software issues this year. Numerous internet and e-commerce companies are tapping funds through the capital market. For the financial intermediaries as well as for the investing public, dot com or 'info' initial offerings are fast becoming attractive to investment alternatives to traditional manufacturing or financial sector offers.With more effective capital markets, for high potential IT stocks, 'critical mass', which in the industrial economy' was primary in ensuring a company's ability to raise capital, will cases to matter. This underlines the manner in which a burgeoning digital economy has led to a redeployment of capital from a concentrated segment to the smaller knowledge entrepreneur.A greater number of venture capitalists actively seeking to fund budding knowledge entrepreneurs. Along with the rise in Net entrepreneurs one has seen the emergence of a new breed of venture capitalists who recognize the potential that resides in these ideas. The emergence and strengthening of the virtual economy necessitates sources of funds at the' ideation' stage where business plans may still be at the in fancy stage and potential not clearly identified.This need is being fulfilled by the incubator funds or the angle investors who hand-hold internet startups and other info tech ventures till the stage at which they can attract bigger investors. Instead of looking at high risk but big ventures, this genre of venture capitalists are looking at investments in companies which have the potential of excellent valuations in the future on the strength of their ideas.which as the following has been related as most crucial in agro-based economy ? 1.Capital steamboat and trains. 2.Technological innovations like plough,etc. 3.Labour and land.....

MCQ-> Read the passage carefully and answer the given questionsThe complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors. . . . The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. . . .Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool. That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist. Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of enquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail. What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.Evidence for this claim can be seen in the way that papers and patents that combine diverse ideas tend to rank as high-impact. It can also be found in the structure of the so-called random decision forest, a state-of-the-art machine-learning algorithm. Random forests consist of ensembles of decision trees. If classifying pictures, each tree makes a vote: is that a picture of a fox or a dog? A weighted majority rules. Random forests can serve many ends. They can identify bank fraud and diseases, recommend ceiling fans and predict online dating behaviour. When building a forest, you do not select the best trees as they tend to make similar classifications. You want diversity. Programmers achieve that diversity by training each tree on different data, a technique known as bagging. They also boost the forest ‘cognitively’ by training trees on the hardest cases - those that the current forest gets wrong. This ensures even more diversity and accurate forests.Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. . . . That’s not likely to lead to breakthroughs.Which of the following conditions, if true, would invalidate the passage’s main argument?
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