Canadian Political Structure Essay -- Canada Government

Primus inter pares ? What are the sources and the limits of the power of the Prime Minister? Primus inter pares†¦ Latin phrase meaning 'the first among equals' or 'first among peers'. The title was also used by Caesar Augustus to hide behind the fact that even though the Senate of Rome was officially governing the Roman Republic, he in practice retained his autocratic power like his predecessors had done. Which of these definitions should we take for Prime Minister of Canada? What are the powers of the Prime Minister? What are the limits to this power? We will see that the powers of the Prime Minister come from the constitution, from making nominations, from being able to call elections. We will also look at the powers that reside in the cabinet system. We will then try and look at some of the limits of this power, and describe how some of these limits can actually be cancelled or reversed Constitution When one looks at the Canadian Constitution, there does not seem to be any mention of the prime minister except for section 49 of the 1982 act which talks about a Constitutional conference that the Prime Minister will need to have with the premiers of the provinces to discuss the changes to the constitution. (White 1998, p.313) But if one looks more closely at the Act of 1867, or if one reads between the lines, one can see in the first paragraphs that it mentions â€Å"a Constitution similar in Principle to that of the United Kingdom† (White 1998, p.277) This, along with sections 11 to 14 indirectly means that Canada will have a Westminster Parliamentary System and that Canada will adopt a system with a Prime Minister and a Cabinet and a Privy Council. Britain already had many years of experience about what the prime minister’s d... ...e, Jean Chrà ©tien. Montreal: Lanctot Editeur, 2002. Levine, Allan Gerald. Scrum wars: the prime ministers and the media. Toronto: Dundurn Press, 1993. Massicotte, Louis. "L’annonce de Jean Chrà ©tien - La dà ©mission forcà ©e d’un premier ministre, les prà ©cà ©dents." le devoir, 7 avril 2010. Savoie, Donald. Governing from the center. Toronto: University of Toronto Press, 1999. —. Power: Where is it ? Montreal: McGill-Queen's University Press, 2010. Simpson, Jeffrey. The Friendly Dictatorship. Toronto: McClelland & Stewart, 2001. Spector, Norman. "prorogation jean chretien did it too." The Globe and Mail, january 9, 2010. Thomas, Paul G. "Governing from the center: Reconceptualizing the role of the PM and the Cabinet." Policy Options, dec 2003-jan 2004: 79-85. White, Walter. Introduction to Canadian Politics and Government 7th edition. Toronto: Harcourt Brace, 1998.

Scholarly Voice And Writing

Some people are aloof to read scholarly articles for the fear of not understanding them. They have this connotation that since the articles are written by those masters in the subject, they could not easily relate to them. Obviously, this notion about scholarly articles does not favor both the readers and the writers. While the readers will continue to elude such kind of writing, the writers on one hand will be limited in proliferating their messages. How could this scenario be avoided? Perhaps it is good to start in solving the problem about the misconceptions of scholarly writing. The writers of these articles could probably hold several degrees in their fields, but it does not mean that what they write are being understood only by those who are also in their same fields. At this point, the role of effective scholarly writing takes place. It is now the responsibility of the writers to convey their message using the most appropriate and effective manner of scholarly writing. What then are the criteria for effective scholarly writing and how does one achieve it? Effective scholarly writing could be best described as having an effective scholarly voice. What then is a scholarly voice? Scholarly voice is simply described as the voice that reflects one’s cognition about a topic. A person having a scholarly voice represents his full grasp on his subject. Because his audience could not see him, the way he wrote will define his voice. His writing is his own voice. Therefore, it is important to know how to have an effective scholarly writing. Scholarly writing is a kind of writing characterized by clarity, conciseness, and precision in words. By clarity it means that the writer should be able to express all of his thoughts in a clear, understandable manner. This could be achieved by following some tips. For example, relying on grammar, spelling, and writing style manuals would add to the clarity of the message. Being concise, on the other hand, could be achieved through giving the readers a comprehensive message. With comprehensiveness, it means that the message should not only be complete, but should reflect objectivity as well. A scholarly writing is based on solid evidence. Finally, preciseness in the use of words means appropriateness in language choice. As Professor Lilburn Hoehn has said, â€Å"Figure out what you want to say and just say it†. An effective scholarly writing is direct to the point and free from wordiness or puffery. Although there are criteria for effective scholarly writing, sometimes outside factors may affect a writer in his writing. One of these is the intended audience. A writer should be sensitive to the basic information about his intended audience—its demographics, its personality, and most importantly, its expectations and needs. As a scholar writer, one should know that his intended audience is mostly comprised of people of his same level of mastery. His writing should be strongly based on evidence because his intended audience is composed of critical and rational thinkers. Thus, the writer should know what he is writing about. It should be true and should matter to his audience. Having described effective scholarly voice and writing, it is now better for me to assess myself. I believe that one of my greatest assets is my keen sense for objectivity. Because of this, I am sure that I will be able to exude one of most important characteristics of effective scholarly writing—objectivity. However, I also believe that one of my weaknesses is being wordy with my writing. Knowing that my intended audience, I should try to eliminate this weakness and convert it to an asset. To do this, I believe that I should expose myself more to samples of scholarly writings. More importantly, practicing my writing skill is the best way to gain my goal. Gaining the skills to write a scholarly article using a scholarly voice seems to be a task hard to attain. While most people are accustomed writing and using layman’s terms, it is still not a valid excuse to elude from learning how to write in a scholarly manner. One should really have the determination to learn the principles of scholarly writing.

What Is a Plus Four Confidence Interval

In inferential statistics,  confidence intervals for population proportions rely upon the standard normal distribution to  determine unknown parameters of a given population given a statistical  sample of the population. One reason for this is that for suitable sample sizes, the standard normal distribution does an excellent job at estimating a binomial distribution. This is remarkable because although the first distribution is continuous, the second is discrete. There are a number of issues that must be addressed when constructing confidence intervals for proportions. One of these concerns what is known as a â€Å"plus four† confidence interval, which results in a biased estimator. However, this estimator of an unknown population proportion performs better in some situations than unbiased estimators, especially those situations where there are no successes or failures in the data. In most cases, the best attempt to estimate a population proportion is to use a corresponding sample proportion. We suppose that there is a population with an unknown proportion p of its individuals containing a certain trait, then we form a simple random sample of size n from this population. Of these n individuals, we count the number of them Y that possess the trait we are curious about. Now we estimate p by using our sample. The sample proportion Y/n is an unbiased estimator of p. When to Use the Plus Four Confidence Interval When we use a plus four interval, we modify the estimator of p. We do this by adding four to the total number of observations, thus explaining the phrase â€Å"plus four. We then split these four observations between two hypothetical successes and two failures, which means that we add two to the total number of successes. The end result is that we replace every instance of Y/n  with (Y 2)/(n 4), and sometimes this fraction is denoted by  p with a tilde above it. The sample proportion typically works very well at estimating a population proportion. However, there are some situations in which we need to modify our estimator slightly. Statistical practice and mathematical theory show that the modification of the plus four interval is appropriate to accomplish this goal. One situation that should cause us to consider a plus four interval is a lopsided sample. Many times, due to the population proportion being so small or so large, the sample proportion is also very close to 0 or very close to 1. In this  type of situation, we should consider a plus four interval. Another reason for using a plus four interval is if we have a small sample size.  A plus four interval in this situation provides a better estimate for a population proportion than using the typical confidence interval for a proportion. Rules for Using the Plus Four Confidence Interval The plus four confidence interval is an almost magical way to calculate inferential  statistics more accurately in that simply adding in four imaginary observations to any given data set, two successes and two failures, it is able to more accurately predict the proportion of a data set which fits the parameters. However, the plus-four confidence interval isnt always applicable to every problem. It can only be used when the confidence interval of a data set is above 90% and the sample size of the population is at least 10. However, the data set can contain any number of successes and failures, though it does work better when there are either no successes or no failures in any given populations data. Keep in mind that unlike the calculations of regular statistics, inferential statistics calculations  rely on a sampling of data to determine the most likely results within a population. Though the plus four confidence interval corrects for a larger margin of error, this margin must still be factored in to provide the most accurate statistical observation.