A Biopsychosocial Assessment In the Yakin-directed film Fresh, a year-old boy -- "Fresh" -- struggles to balance school and a tumultuous home life with the drug-running activities that allow him to make and save money. Though Fresh is intelligent, ambitious, and highly motivated to rise above his current station in life, as an African-American living in the crime-driven projects, his perceived opportunities for advancement are limited. As a result, Fresh makes money in the only way he knows how; as an inner-city drug mule for the number one suppliers of heroin -- "smack" -- and cocaine, referred to as "base.
This syllogism can be combined with an observation about the behavior of increasingly large samples. From calculations of the sampling distribution, it can be shown that as the sample size increases, the probability that the sample frequency is in a range which closely approximates the population frequency also increases.
We can then apply the proportional syllogism to samples from a population, to get the following argument: Most samples match their population S is a sample. Therefore, S matches its population, with high probability. This is an instance of the proportional syllogism, and it uses the general result about samples matching populations as the first major premise.
Both Williams and Stove claim that this amounts to a logical a priori solution to the problem of induction. A number of authors have expressed the view that the Williams-Stove argument is only valid if the sample S is drawn randomly from the population of possible samples—i.
Sometimes this is presented as an objection to the application of the proportional syllogism. The claim is that the proportional syllogism is only valid if a is drawn randomly from the population of Ms. Certainly if you have reason to think that your sampling procedure is more likely to draw certain individuals than others—for example, if you know that you are in a certain location where there are more of a certain type—then you should not apply the proportional syllogism.
But if you have no such reasons, the defenders claim, it is quite rational to apply it. Certainly it is always possible that you draw an unrepresentative sample—meaning one of the few samples in which the sample frequency does not match the population frequency—but this is why the conclusion is only probable and not certain.
The more problematic step in the argument is the final step, which takes us from the claim that samples match their populations with high probability to the claim that having seen a particular sample frequency, the population from which the sample is drawn has frequency close to the sample frequency with high probability.
This would mean that for any given sample, it is highly credible that the sample matches its population. But this is exactly the slide that Williams makes in the final step of his argument. Maher argues in a similar fashion that the last step of the Williams-Stove argument is fallacious.
In fact, if one wants to draw conclusions about the probability of the population frequency given the sample frequency, the proper way to do so is by using the Bayesian method described in the previous section.
But, as we there saw, this requires the assignment of prior probabilities, and this explains why many people have thought that the combinatorial solution somehow illicitly presupposed an assumption like the principle of indifference.
The Williams-Stove argument does not in fact give us an alternative way of inverting the probabilities which somehow bypasses all the issues that Bayesians have faced.
But it is of course also possible to take on the second horn instead. One may argue that a probable argument would not, despite what Hume says, be circular in a problematic way we consider responses of this kind in section 4. Or, one might attempt to argue that probable arguments are not circular at all section 4.
Some have argued that certain kinds of circular arguments would provide an acceptable justification for the inductive inference. First we should examine how exactly the Humean circularity supposedly arises. Take the simple case of enumerative inductive inference that follows the following pattern X: Most observed Fs have been Gs Therefore: Most Fs are Gs.
Hume claims that such arguments presuppose the Uniformity Principle UP. According to premises P7 and P8this supposition also needs to be supported by an argument in order that the inductive inference be justified. We know that it works, because past instances of arguments which relied upon it were found to be successful.
This alone however is not sufficient unless we have reason to think that such arguments will also be successful in the future. That claim must itself be supported by an inductive argument S:Essay Examples.
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In the case of "interpreting allegorically," Theagenes appears to be our earliest example. Among the best-known examples of allegory, Plato's Allegory of the Cave, The Complete Swan .
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To Kill a Mockingbird. Of Mice and Men. Shakespeare. first wish and to keep the other two in case of an emergency. Taking a thorn he pricked his tongue with it, to remind himself not to utter rash wishes aloud.
Then holding the third leaf and gazing round the swan's head rested against her cheek. She was weeping, and as he came nearer he saw that tears were rolling, too, from the swan's eyes. Jun 08, · The case study of Lee’s music (Kong, ) is used as an example to emphasize the viewpoint that globalization strengthens localization or in other words, if there is little interaction between the global and local sphere, the local identification is quiet.
Lee is a successful Singaporean artist and songwriter in the local and regional markets.