Wednesday, October 30, 2019

Jury Selection Essay Example | Topics and Well Written Essays - 1000 words

Jury Selection - Essay Example Written by Neil Kressel, a social psychologist at New Jersey's William Paterson University, and his wife Dorit, a practicing attorney, this book provides an even-handed accounting of the methods and ethical issues of the phenomenon called jury consultancy and its possible implications for American justice. It provides a discussion regarding the use of jury consultants in sensitive matters such as race and answers the questions: What do jury consultants do Are their elaborate efforts to assist lawyers in the jury selection process by identifying attitudes, values, and would-be demographic predictors merely benign efforts to screen for biases that could jeopardize fair trials, as practitioners like to claim Scientific Jury Selection is a well-written volume that reviews the research and issues surrounding scientific jury selection. The authors examine the many factors and methods involved in this process and provide a balanced and comprehensive review of the literature as well as raise important scientific and ethical questions. Chapters review such factors as methods of acquiring information and applying those methods to the actual process of jury selection. The volume raises substantial issues about the accuracy and efficacy of the selection process, as well as its ethical and legal implications. In addition, it provides the basis for the psychological methods used. 4. A. Austin (1984). Complex Litigation Confronts the Jury System, 103-104. Greenwod Press, US. Austin provides a case study in which one could gain valuable insight into the workings of jury consultancy and provides an analysis and possible implications of the methods used thru the case study presented. 5. Leci, L., Snowden, J. and Morris, D (2004). "Using Social Science Research to Inform and Evaluate the Contributions of Trial Consultants in the Voir Dire." Journal of Forensic Psychology Practice 4.2 (2004) 67-78 The authors argue that the jury selection methods commonly employed by trial consultants and lawyers in the voir dire process are fraught with problems because they do not employ standardized assessments. This commentary provides and advocates the advantages of employing standardized, reliable, and validated measures of pretrial juror bias to more effectively conduct the voir dire, and we delineate some of the methods by which this can be accomplished. 6. Lieberman, Joel D., and Bruce D. Sales (2007). "Overall Effectiveness of Scientific Jury Selection" in PsycINFO. Washington DC, US: American Psychological Association, 2007. Lieberman and Sales provides a discussion on matters of jury consultancy such as the Purpose and effectiveness of the Voir Dire, influence of demographic factors, influence of Personality and Attitudes, in-court questioning of prospective jurors and ethical and professional issues in Scientific Jury Selection. 7. Van Wallendael, Lori, and Brian Cutler (2004).

Sunday, October 27, 2019

Emotion Recognition From Text-a Survey

Emotion Recognition From Text-a Survey Ms. Pallavi D. Phalke , Dr. Emmanuel M. ABSTRACT Emotion is a very important facet of human behaviour which affect on the way people interact in the society. In recent year many methods on human emotions recognition have been published such as recognizing emotion from facial expression and gestures, speech and by written text. This paper focuses on classification of emotion expressed by the online text, based on predefined list of emotion. The collection of dataset is the basic step, which is collected from the various sources like daily used sentences, user status from various social networking websites such as  facebook and twitter. Using this data set we target only on the keywords that show human emotions. The targeted keywords are extracted from the dataset and translated into the format which can be processed by the classifier to finally generate the Predicting model which is further compared by the test dataset to give the emotions in the input sentences or documents. Keywords— Affective Computing, Classification, Document Categorization, Emotion Detections. INTRODUCTION Recently much research is going on in emotion recognition domain. Recognition of emotions is very useful to human-machine communication. Many kinds of the communication system can react properly for the humans emotional actions by applying emotion recognition techniques on them. These systems include dialogue system, automatic answering system and robot. The recognition of emotion has been implemented in many kinds of media, such as image, speech, facial expressions, signal, textual data, and so on. Text is the most popular and main tool for the human to convey messages, communicate thoughts and express inclination. Textual data make it possible for people to exchange opinions, ideas, and emotions using text only. Therefore the research for recognizing from the textual data is valuable. Keyword-based approach to the proposed system since the keyword-based approach shows high recognizing accuracy for emotional keywords. Interaction between humans and computers has been increased with increase in development of information technology. Recognizing emotion in text from document or sentences is the first step in realizing this new advanced communication which includes communication of information such as how the writer/speaker feels about the fact or how they want the reader/listener to feel. Analyzing text, detecting emotions is useful for many purposes, which includes identifying what emotion a newspaper headline is trying to evoke, identifying users emotion from their statuses of different social networking sites, devising dialogue systems that respond appropriately to different emotional states of the user and identifying blogs that express specific emotions towards the topic of interest. List of emotions and words that are indicative of each emotion is likely to be useful in identifying emotions in text because, many times different emotions are expressed by different words. For example cry and glo omy are indicative of sadness, boiling and shout are indicative of anger, yummy and delightful indicate the emotion of joy. To capture emotion from text document we require the classification which aims at presume the emotion conveyed by the documents based on predefined lists of emotion, such as Joy, Anger, Fear, Disgust, Sad and Surprise. This emotion recognition approach is mainly focused on two main tasks. 1) The test data that is text document collected from any news articles, user statuses from different social networking sites etc. required for understanding the emotions evoked by words. This is because a different word arouses different emotions comprehended from our day to day experiences. For this purpose, need is to enhanced dictionary with emotion word from ISEAR, WorldNet Affect to improve in result. 2) Need for text normalization to handle negation, since the scope of words is larger in this scenario, the usage of words and their diverted form is large too. So these problems need to be solved properly. The next part of this paper is organised as follows: Section II discusses a survey of emotion detection from text, Section III describes different algorithms on different datasets for emotion recognition, Section IV briefly compares proposed work followed by experimental study with result in section V and Section V concludes the paper. THE SURVEY OF EMOTION DETECTION FROM TEXTS Definitions about emotion, its categories, and their influences have been an important research issue long before computers emerged, so that the emotional state of a person may be inferred under different situations. In its most common formulation, the emotion detection from text problem is reduced to finding the relations between specific input texts and the actual emotions that drives the author to type/write in such styles. Intuitively, finding the relations usually relies on specific surface texts that are included in the input texts, and other deeper inferences that will be formally discussed below. Once the relations can be determined, they can be generalized to predict others’ emotions from their articles, or even single sentences. At the first glance, it does not seem to involve so many difficulties. In real life, different people tend to use similar phrases (i.e. â€Å"Oh yes!†) to express similar feelings (i.e. joy) under similar circumstances (i.e. achieving a goal); even they native languages are different, the mapping of such phrases from each language may be obvious. More formally, the emotion detection from text problem can be formulated as follows: Let E be the set of all emotions, A be the set of all authors, and let T be the set of all possible representations of emotion-expressing texts. Let r be a function to reflect emotion e of author a from text t, i.e., r: A Ãâ€" T → E and the function r would be the answer to our problem. The central problem of emotion detection systems lies in that, though the definitions of E and T may be straightforward from the macroscopic view, the definitions of individual element, even subsets in both sets of E and T would be rather confusing. On one hand, for the set T, new elements may add in as the languages are constantly evolving. On the other hand, currently there are no standard classifications of â€Å"all human emotions† due to the complex nature of human minds, and any emotion classifications can only be seen as â€Å"labels† annotated afterwards for different purposes. As a result, before seeking the relation function r, all related research firstly define the classification system of emotion classifications, defining the number of emotions. Secondly, after finding the relation function r or equivalent mechanisms, they still need to be revised over time to adopt changes in the set T. In the following subsections, we will present a classification of emotion detection methods proposed in the literature, based on how detection are made. Although they can all be classified into content-based approaches from the point of view of information retrieval, their problem formulation differs from each other: 1. Keyword-based detection: Emotions are detected based on the related set(s) of keywords found in the input text; 2. Learning-based detection: Emotions are detected based on previous training result with respect to specific statistic learning methods; 3. Hybrid detection: Emotions are detected based on the combination of detected keyword, learned patterns, and other supplementary information; Besides these emotion detection methods that infer emotions at sentence level, there has been work done also on detection from online blogs or articles [1][2]. For example, though each sentence in a blog article may indicate different emotions, the article as a whole may tend to indicate specific ones, as the overall syntactic and semantic data could strengthen particular emotion(s). However, this paper focuses on detection methods with respect to single sentences, because this is the foundation of full text detection. A. KEYWORD-BASED METHODS Keyword-based methods are the most intuitive ways to detect textual emotions. To approximate the set T, since all the names of emotions (emotion labels) are also meaningful texts, these names themselves may serve as elements in both sets of E and T. Similarly, those words with the same meanings of the emotion labels can also indicate the same emotions. The keywords of emotion labels constitute the subset EL in set T, where EL also classifies all the elements in E. The set EL is constructed and utilized based on the assumption of keyword independence, and basically ignores the possibilities of using different types of keywords simultaneously to express complicated emotions. Keyword-based emotion detection serves as the starting point of textual emotion recognition. Once the set EL of emotion labels (and related words) is constructed, it can be used exhaustively to examine if a sentence contains any emotions. However, while detecting emotions based on related keywords is very straightforward and easy to use, the key to increase accuracy falls to two of the pre-processing methods, which are sentence parsing to extract keywords, and the construction of emotional keyword dictionary. Parsers utilized in emotion detection are almost ready-made software packages, whereas their corresponding theories may differ from dependency grammar to theta role assignments. On the other hand, constructing emotional keyword dictionary would be naval to other fields [3]. As this dictionary collects not only the keywords, but also the relations among them, this dictionary usually exists in the form of thesaurus, or even ontology, to contain relations more than similar and opposite ones. Semi-automatic construction of EL based on WorldNet-like dictionaries is proposed in [4] and [5]. As was observed in [6], keyword-based emotion detection methods have three limitations described below. 1) AMBIGUITY IN KEYWORD Though using emotion keywords is a straightforward way to detect associated emotions, the meanings of keywords could be multiple and vague. Except those words standing for emotion labels themselves, most words could change their meanings according to different usages and contexts. It is not feasible to include all possible combinations into the set EL. Moreover, even the minimum set of emotion labels (without all their synonyms) could have different emotions in some extreme cases such as ironic or cynical sentences. 2) INCAPABILITY OF RECOGNIZING SENTENCES WITHOUT KEYWORDS As Keyword-based approach is totally based on the set of emotion keywords, sentences without any keywords would imply like they don’t contain any emotions at all, which is obviously wrong. 3) LACK OF LINGUISTIC DATA Syntax structures and semantics also affect on expressed emotions. For example, â€Å"He laughed at me â€Å"and â€Å"I laughed at him† would suggest different emotions from the first person’s point of view. Therefore, ignoring linguistic information also create a problem to keyword-based methods. B. LEARNING-BASED METHODS Researchers using learning-based methods attempt to formulate the problem differently. The original problem that determining emotions from input texts has become how to classify the input texts into different emotions. Unlike keyword-based detection methods, learning-based methods try to detect emotions based on a previously trained classifier, which apply various theories of machine learning such as support vector machines [7] and conditional random fields [8], to determine which emotion category should the input text belongs. However, comparing the satisfactory results in multimodal emotion detection [9], the results of detection from texts drop considerably. The reasons are addressed below: 1) DIFFICULTIES IN DETERMINING EMOTION INDICATORS The first problem is, though learning-based methods can automatically determine the probabilities between features and emotions, learning-based methods still need keywords, but just in the form of features. The most intuitive features may be emoticons, which can be seen as author’s emotion annotations in the texts. The cascading problems would be the same as those in keyword-based methods. 2) OVER-SIMPLIFIED EMOTION CATEGORIES Nevertheless, lacking of efficient features other than emotion keywords, most learning-based methods can only classify sentences into two categories, which are positive and negative. Although the number of emotion labels depends on the emotion model applied, we would expect to refine more categories in practical systems. C. HYBRID METHODS Since keyword-based methods with thesaurus and naà ¯ve learning-based methods could not acquire satisfactory results, some systems use a hybrid approach by combining both or adding different components, which help to improve accuracy and refine the categories. The most significant hybrid system so far is the work of Wu, Chuang and Lin [6], which utilizes a rule-based approach to extract semantics related to specific emotions, and Chinese lexicon ontology to extract attributes. These semantics and attributes are then associated with emotions in the form of emotion association rules. As a result, these emotion association rules, replacing original emotion keywords, serve as the training features of their learning module based on separable mixture models. Their method outperforms previous approaches, but categories of emotions are still limited. D. SUMMARY AND CONCLUSIONS As described in this section, much research has been done over the past several years, utilizing linguistics, machine learning, information retrieval, and other theories to detect emotions. Their experiments show that, computers can distinguish emotions from texts like humans, although in a coarse way. However, all methods have certain limitations, as described in the previous subsections, and they lack context analysis to refine emotion categories with existing emotion models, where much work has been done to put them computationalized in the domain of believable agents. On the other hand, applications of affective computing would expect more refined results of emotion detection to further interact with users. Therefore, developing a more advanced architecture based on integrating current approaches and psychological theories would be in a pressing need. III. ALGORITHMS USED IN EMOTION RECOGNITION A brief summary of the various works for emotion recognition discussed in this paper are presented in Table1. Table 1: Results and feature-set comparison of algorithms IV.EMOTION RECOGNITION IN SOCIAL COMMUNICATION The block diagram of the emotion recognition system studied in this paper is depicted in Figure 1.It contains three main modules: Affective communication unit, Data Aggregator, Emotion Recognition Engine and recognized emotion class as an output. Figure 1 : Block diagram of emotion recognition system for Affective communication AFFECTIVE COMMUNICATION UNIT Affective Communication Unit is nothing but the users account in any social networking site (tweeter or facebook). This system take input from these two social networking sites. DATA AGGREGATOR Data Aggregator collects user tweets and status from tweeter and facebook. These tweets/status serve as an input to Emotion Recognition Engine. EMOTION RECOGNITION ENGINE Emotion Recognition Engine including Bayesian Network classifier categorizes incoming data into 3 types of emotions: happiness, sadness, and neutral, because this system mainly focuses on finding stress level of user. It is broken up into 2 major phase: Training Phase and Testing Phase. Training phase consist of five important parts: The Training Dataset, Keyword Extraction, Keyword conversion, Training Model and Predicting Model. Before it generate the predicting model or file, training phase get the training dataset from which it extracted the keyword from the emotion training date, and convert the keyword using keyword conversion into the format that can be processed by the classifier in the Training Model. Testing phase which is also called predicting phase consist of Testing dataset, Keyword extraction, Keyword conversion and predict model. The testing phase extract the Keyword from the given sentence, which was the input from the keyboard and then translate the keyword (word of natural language) using the Keyword conversion into the format that can be processed and then we compare it with a predicting file in predict module and finally gives the output as appropriate emotion expressed by the text. VI.CONCLUSION The proposed system is able to recognize the happy and sad state of a person from his tweets posted on tweeter from his mobile. The experimental results Shows that the we get better accuracy using Naive Bayes classifier than that of Support Vector Machine. VII. REFERENCES [1] 2. Tim M.H. Li, Michael Chau, Paul W.C. Wong, and Paul S.F. YipA Hybrid System for Online Detection of Emotional Distress PAISI 2012, LNCS 7299 Springer-Verlag Berlin Heidelberg 2012M, 73–80. [2] Abbasi, A., Chen, H., Thoms, S., Fu, T.: â€Å"Affect Analysis of Web Forums and Blogs Using Correlation Ensembles.† IEEE Transactions on Knowledge and Data Engineering (2008) ,1168–1180. [3] T. Wilson, J. Wiebe, and R. Hwa, â€Å"Just how mad are you? Finding strong and weak opinion clauses,† Proc. 21st Conference of the American Association for Artificial Intelligence Jul. 2007, 761-769. [4] D. B. Bracewell, â€Å"Semi-Automatic Creation of an Emotion Dictionary Using WordNet and its Evaluation,† Proc. IEEE conference on Cybernetics and Intelligent Systems, IEEE Press, Sep. 2008, 21-24. [5] J. Yang, D. B. Bracewell, F. Ren, and S. Kuroiwa, â€Å"The Creation of a Chinese Emotion Ontology Based on HowNet†, Engineering Letters, Feb. 2008,166-171. [6] C.-H. Wu, Z.-J. Chuang, and Y.-C. Lin, â€Å"Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models,† ACM Transactions on Asian Language Information Processing Jun. 2006, 165-183. [7] Z. Teng, F. Ren, and S. Kuroiwa, â€Å"Recognition of Emotion with SVMs,† in Lecture Notes of Artificial Intelligence Eds.Springer, Berlin Heidelberg, 2006,701-710 . [8] C. Yang, K. H.-Y. Lin, and H.-H. Chen, â€Å"Emotion classification using web blog corpora,† Proc. IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, Nov. 2007, 275-278. [9] C. M. Lee, S. S. Narayanan, and R. Pieraccini, Combining Acoustic and Language Information for Emotion Recognition, Proc. 7th International Conference on Spoken Language Processing (ICSLP 02), 2002, 873-876. [10]http://www.affectivesciences.org/reserachmaterial [11] http://www.weka.net.nz/

Friday, October 25, 2019

Robert Frost :: essays research papers

Case Study on Robert Frost From the later 1800’s (1874) to the middle 1900’s (1963), Robert Frost gave the world a window to view the world through poetry. From “A Boy’s Will'; to “Mountain Interval,'; he has explored many different aspects of writing. Giving us poems that define hope and happiness to poems of pure morbid characteristics; all of Robert Frost’s poems explain the nature of living. But why does Frost take two totally different views in his poems? Is it because of his basic temperament or could it be that his attitude towards life changed in his later years?   Ã‚  Ã‚  Ã‚  Ã‚  Throughout the life of Robert Frost, many different kinds of struggles where manifested in his life that hampered his every thought. Some say that Frost went from a “bright and sunny day'; to “a dreary night.'; But even with all of the animosities that plagued his life, Robert Frost evolved to become one of America’s greatest poets.   Ã‚  Ã‚  Ã‚  Ã‚  Frost’s poems were not respected in the United States at the time that he first began writing. But after a brief stay in England, Frost emerged as one of the most extraordinary writers in his time. Publishing A Boy’s Will and North Of Boston, Frost began his quest.   Ã‚  Ã‚  Ã‚  Ã‚  In the book A Boy’s Will, Frost writes poems of hope and beauty. “Love and a Question,'; illustrates the optimistic view of a bridegroom trying to help a poor man. He thinks that he should help him, but not knowing if he can. His heart shows compassion but his minds shows logic. The conclusion of this poem shows not true ending, but leaves the reader in a state of imagining what was to happen to the poor man.   Ã‚  Ã‚  Ã‚  Ã‚  So much of the true Frost can be seen in his poem, “The Vantage Point'; (A Boy’s Will). In these verses, Frost reveals his basic interests – mankind and nature. What’s more, he clearly exposes his strategy of immersing himself in nature until he begins to need social relations again; likewise, when he has his fill of mankind, he retreats back to the comfort and solitude of nature. “And if by noon I have too much of these (men), I have but to turn on my arm, and so, the sun-burned hillside sets my face aglow.'; Frost wants neither mankind nor nature to the exclusion of the other. Rather be prefers to spend time with each, satisfied that he will know when he’s had his fill.

Thursday, October 24, 2019

Is the Destruction of the Amazon Rainforest a price Worth Paying for Brazil’s Economic development?

The tropical rainforest is undoubtly one of the most fascinating biomes located around the world. These ecosystems are located over eighty five countries near the equator and one of the most prominent rainforest is known the Amazonia. The Amazon Rainforest not only situated over many countries including Brazil, Colombia, Peru, Venezuela, Ecuador, Bolivia, Guyana, Suriname, and French Guiana but it is also known to be home of over 50% of the Earth's species and approximately one third of the world's tree grows in Amazonia. The rainforest has been estimated to cover seven million square kilometres and at least forty thousand plant species have been classified, which makes the Amazon rainforest a resourceful bio diverse ecosystem. This biodiversity and large land has attracted potential companies, especially from Brazil to take advantage of the Amazon Rainforest. Although Brazil is one of the world's fastest developing countries and the richest country in South America, it is still considered as a middle income ‘LEDC' [Less Economically Developed Country] and its rapid population growth has increased the demand for natural resources. Brazil has remains to solve these problems by the deforestation of the precious Amazon Rainforest. Deforestation can be defined as the removal of the forest stands for human activities, such as agriculture and unfortunately, already 25% of the Amazon Rainforest has been cleared in 40 years and 40 hectares is being cleared per minute. At this rate, the Amazonia will be gone in 30 years! Is the destruction of the Amazon Rainforest a price worth paying for? The Amazonia is famous for being the largest and more diverse ecosystem on Earth. Almost everyday, there are new species being discovered and the rainforest is the habitat to some of the most essential plants to the medical world, for example, the rosy periwinkle which is known to provide drugs to help cure leukaemia. The diverse ecosystem is caused indirectly by the location of the rainforest. The centre of the Amazon Rainforest is located near and on the equatorial line. This means that the Amazonia receives concentrated amount of sun rays, making the rainforest very hot. The equatorial location means that the temperature of the climate is very high and constant with twelve hours of sunshine. Usually, the morning temperature can reach up to 32iC and in the evening, it rarely goes below 22iC. The high concentration of the sun rays absorbed by the Earth also encourages conventional rainfall daily. This is when the land gets real hot, that the warm air around it gets enough energy to rise. As the warm air rises, it gets cooled by the atmosphere because it is much colder the higher you travel. The cooled air then condenses into clouds and later heavy rainfalls. Due to daily conventional rainfalls, the Amazon rainforest is a wet, humid area as well as being hot. The Amazon rainforest has two seasons: the dry season between June to December and wet season from January to May, when May can have a rainfall of around 259 mm. The abiotic factors, the non living features of earth like the sun, cause the Amazon Rainforest to have a very warm and humid climate, also known as a tropical climate. The biotic factors, the living factors of an ecosystem like plants, are just as important as the climate in maintaining the tropical rainforest. The diagrams on the next page show how vital plant life is to support the other plants in the ecosystem. After a plant dies or leaves fall to the forest floor, decomposers in the soil break down the dead matter into humus. Humus is important to forest land chemically and physically. Humus can increase the amount of moisture a soil holds and can help the soil have a better structure. Humus also acts as a catalyst because it has actives sites that help produce nutrients. The more nutrients a soil has, the better a plant would be able to grow and survive. This also shows why deforestation endangers the ecosystem. The second cycle illustrates shows that when trees are cleared away, causing less leaves and dead organisms to decay onto the soil, less humus is produced. This cause there to be fewer nutrients because more was taken away from the plant than returned back to the soil as the tree was removed. Fewer nutrients means that the soil is less fertile than originally, so new plants grow with a weaker quality and less chance of surviving. This encourages soil erosion because there no trees to protect the soil from being moved away. Brazil has the eighth largest economy in world and is the wealthiest in its continent. Unfortunately, the country can only be considered as a NIC [newly developed country] or LEDC and is still located under the Brandt line. The Brandt line is a ‘socio-economic' line that divides the more economically developed countries, the north, from the less economically developed countries, the south. This is understandable because the social and economics indicators are considerably low, especially compared to the UK. Economic Social Country GNP Per Capita ($) Birth Rate Death Rate Natural Increase Life Expectancy Population Per Doctor Brazil 3 640 22 7 15 68 1 000 UK 18 700 12 11 1 77 300 Figure 2: This table shows the measure of development in the year 2000. Figure 2 expands on Brazil and UK's indicators of development. It shows that in the year 2000 that the natural increase of the population of Brazil, which the government has to support the demands of, was fifteen times larger than the UK. The NIC also has quite a low life expectancy and the people in England are likely to live approximately 9 years longer than Brazilians. This may be due to the fact that Brazil's education system is quite low for an NIC; its adult literacy rate is 84%, compared to the UK's 99%. This means that there are fewer professional workers such as doctors and engineers, which causes of there being only one doctor per thousand people. The chart also shows the Gross Nation Product [GNP] per Capita [per person]. The value of GNP per Capita can be described as the total value of services and goods produced by a country in a year divided by the residents of the country. The GNP includes the residents of the country living abroad and excludes non-residents of a country. Another common measure of economical wealth is the Gross National Product [per capita], which can be defined as the total value of services and goods produced divided the people in the country that year. The GNP and GDP are similar, the only difference they have is who they think the ‘capita' is, which means that the value of GNP and GDP are very similar. Figure 2 shows that the GNP is significantly low as it is nearly five times smaller than the value of UK's GNP. However, Brazil has shown a rapid increase of GNP and GDP. In the year 2006, Brazil's GDP per capita was $8,800, and then it increased by $900 to $9,700. This is evidence that Brazil's economy is growing. The United Nation uses the Human Development Index [HDI] to measure development. It was created in 1990, but was initially used three years later, because it combines social and wealth indicators to produce a more insightful measure of development. The HDI looks at the three factors of human progress: ==> A long healthy life [measured by life expectancy] => Education and Knowledge [measured by adult literacy and years spent in school] ==> Standard of living [measured by GDP per capita] Each of the three factors are given a ‘score' from 0. 000 [worst] to 1. 000 [best], which can be worked out through calculations according to each factor, then the average of the three scores gives the country its HDI. The countries can be also ranked according to their HDI. Figure 3 shows that there has been, although small, change in Brazil's HDI. In the data published in 2005, Brazil was ranked 63rd with a HDI of 0. 92 and then in the data published in 2007, it was awarded with a HDI of 0. 800 even though it moved down 7 ranks. A HDI value is just about considered as ‘high' and it shows that Brazil has reasonable standard of living. The data also shows that Brazil is in competition for other countries for better human development because it is going down in ranks although it is becoming more industrialised. Brazil must continue developing both socially and economically to be considered as an average MEDC. Brazil's rapid advancement is all due to trade. In 2006, Brazil had import value was $91. 4 billion while it had exported $137. 8 billion worth of goods. The country had gained approximately 150. 8% of its import and means that Brazil experiences trade surpluses, which is when the money from exports is greater than money from imports. The government can spend the extra money on education, medical health care and building the citizens of Brazil. Brazil has plantations that produce vegetation that are able to grow in tropical climates. These exported crops include soy beans, coffee, cocoa and sugar cane. The industries of Brazil have grown noticeably well and 74% of Brazil's goods are [semi] manufactured such as transport equipment, footwear, coffee, autos. There are also quite a few cattle ranches in Brazil which provide beef in MEDC, especially USA. Figure 4 shows that 23% of all occupations are primary jobs, work that deals with collection or producing natural resource from the earth, 24% are secondary activities, work to do with manufacturing and 53% have tertiary jobs that deal with providing services. Approximately one quarter of Brazilians have primary sector careers because they do not require a lot of skills, so majority of the population can do it, and Brazil has excellent resources for land and wood. However, a majority of jobs are in the tertiary sector because Brazil has a rising population, so there must be enough services to satisfy the demanding population, and Brazil is a popular tourist spot, so some jobs are created by tourism such as tour guides. One third of Brazil's GDP comes from the countries assorted range of industries. 4% of workers are employed in the manufacturing sector and these people work in automobile, air craft, steel, petrochemicals other durable good factories. The LEDC has to import goods such as machinery, electrical and transport equipment, chemical products, oil, automotive parts, and electronics for its industries. The Amazon rainforest is under threat from the increasing rate of deforestation. Most of the land deforested is being used by Brazil's industries. Trees in the rainforest, such as mahogany, have been cut down so they can be exported or used for construction or furniture making. Not all the plants cleared are used in the industries; some are wasted to make land for cattle ranches. These large cattle ranches usually have contracts with American fast food chains, so the restaurants can buy the beef cheaply. The Amazon rainforest also has the perfect temperature for growing tropical crops, so farmers use the forest land as pasture to grow sugar canes, soy beans, and coffee beans. The beef and crops can be exported to MEDCs as trade goods. The Amazonia is also rich in bauxite, rock containing aluminium, so there have been large mining operations. The aluminium are then extracted from the bauxite and then used in industries. Mr. Enriquez, Chief of the Trombetas Bauxite Mine explained that ‘the bauxite [they] mine is used in Brazil and sold to rich countries around the globe. It is used to make aluminium, which is used in aircrafts, production, soft drink cans and hundreds of other products. ‘ However, mining involves digging up the land and changing its landscape; it is to be expected that mining would ruin the soil and the plants in the Amazon. Mr Enriquez also said ‘However, it is inevitable that some rainforest will be destroyed in large scale extraction of raw materials. Mining of this kind is of vital national importance to the Brazilian economy. The sale pf bauxite and iron core brings billions into Brazil and creates hundreds of thousands of jobs. ‘ Although Mr. Enriquez is defending his organization, it is true that mining creates jobs, especially for unskilled people, and that it brings money into the country. Brazil is the fifth most populous country. Its growing population and those suffering under poverty are forced to live in favela, which are small, cramped houses with limited sewage and electricity made from scrap building materials. The government has made a new policy to provide land for homeless Brazilians to prevent shanty town conditions. The land provided comes from the clearings of Amazon rainforest. The people are expected to live in the forest and have deal with their own farm. Pedro, a pioneer settler, participated in the scheme because he was destitute. Although he was hoping for a reasonable life, Pedro was unable to continue living in the forest. He said â€Å"It is very remote in the forest and once I had cleared my land the soil fertility declined so growing crops is not easy. † Pedro was considering moving to the city like most work seekers. This shows that the government scheme was not success for everyone and that clearing away the land for homes was not a good idea because people had no knowledge on farming and trade. Amerindians are known as the first people who have used the Amazon Rainforest's raw materials. They live in houses called ‘malocas' and they are dependent on the rainforest resources. Amerindians use the method of shifting cultivation as a way of farming and living in the Amazon Rainforest, they live in one area of the forest and farm there until the fertility and production of the soil has declined, which can last for five years. The Amerindians then leave the area for another and continue farming there so the previous area can recover its fertility. Unfortunately, when the Europeans discovered the Amazon Rainforest and its resources, the Amerindians were in danger. A Tukano Indian explained that when the ‘outsides begun to destroy' the forest, their tribe had to go deeper into the forest for their own safety or give up their lifestyle to live on reservations. It is unethical to sacrifice the life of a whole community for land and profit, especially of a community that helped the forest as well depending on it, unlike companies which just extract resources. If the rate of deforestation continues, Amerindians would have to give up their way of life by either being killed in the process or by being forced to move. Brazil's organizations have to understand that deforestation comes with consequences. All ecosystems are delicate and each species of plant and animals depend on each other for food, shelter, reproduction and if one species is harmed than others will be harmed too like a water ripple. Deforestation directly affects trees in the Amazon rainforest. Cutting down and exporting trees can make some species of hardwood plants to be vulnerable to extinction, for example, mahogany is a popular timber used for furniture however if the deforestation continues, mahogany may become scarce. Logging also takes away the habitats of Amazon's animals, giving them a less chance of survival and killing them. Majority of Amazon's plants have not been discovered, so there may be a species of plant out there that can made into drugs to cure feared illnesses, such as Aids and cancer. Deforestation contributes directly and indirectly to extinction of thousands of unknown species. Deforestation also stops the humus/ nutrient cycle from continuing. Since the tree is taken down before it can die or shed leaves to decay, the decomposers do not have any dead matter to break down the so the nutrients taken from tree cannot be returned. As there will be fewer nutrients in the soil than before, the soil will become less fertile and the tree plants in the soil after will develop weaker than the initial tree. The weaker tree would then be broken down, and the process will repeat until the soil is too infertile to produce any vegetation. There would be no roots or plants to hold the roots together, which would cause increase in soil erosion. Soil erosion is the movement of soil, and deforestation can cause excessive erosion, because there are no plants or trees to protect the soil, and this process may cause flooding and then difficulty in farming as the landscape has changed and the water will be too saturated for some vegetation. Deforestation can also cause a more dramatic change, like desertification, if trees are being cut in a rapid rate. Like animals and humans, trees also respire as well as photosynthesize, so the pores of the leaves give out water vapour to the atmosphere, so with the decreasing number of trees, the lack of water vapour in the atmosphere can encourage desertification. Another effect of deforestation is global warming. Trees are the number one source of reducing carbon dioxide because they take it in for photosynthesis and produce less carbon dioxide for the reactions than they took in. Burning trees not stops the reduction of carbon dioxide, but it also contributes because some of the plants cut are burned. Burning trees release more carbon dioxide and contribute to global warming. Also, Amazonia is considered to be the source of over 20% of the world's oxygen, as product of photosynthesis, and the forest has been described as ‘lungs of the earth'. Therefore, deforestation indirectly causes an increase in pollution and decrease in oxygen. The problem of deforestation can be solved by using sustainable methods of extracting and educating both the companies and people about how delicate the ecosystem is. A sustainable method is one that satisfies the needs of the present population without compromising the need of the future generation. A common technique used to preserve ecosystems is by establishing National Parks and Forest Reserves to protect untouched part of the forest. These reserves may depend on both the government and charity and will make sure that the protected areas are kept as natural as possible while educating people about the importance of foliage in the rainforest. Laws on companies extracting raw materials from the Amazon rainforest must be made stricter than before. Logging grants should only be available to those who plant the same number of trees they cut down, which is a sustainable method, so there no loss in the number of trees. The timber trade companies should also be restricted by reducing trades of endangered plants. Also, any organizations that burn a large amount of trees should be warned that they must reduce the mass burnings so they do not contribute to global warming. Any companies that do not obey the law and does not help preserve the Amazon Rainforest should be heavily fined. If the government wants to continue their scheme to send dispossessed Brazilians to the forest, they should educate them about how to keep their soil fertile by keeping foliage and natural compost, so they will be able to manage a small farm. In conclusion, Amazon rainforest's location has caused to have a constantly hot climate with a wet and dry season. The rainforest is densely population with trees and other plants species and is home to around 50% of the world's animals and plants. The rainforest is a vital resource for plants that are used as drugs for serious illness like leukaemia. However, Brazil has been using the rainforest to extract raw materials for export, land for cattle ranch and to provide land for homeless Brazilians. These exports have caused Brazil to experience trade surpluses that help develop the country. Unfortunately, deforestation is affecting more than just Brazil. The rate of deforestation is contributing to global warming and taking away a huge source of oxygen and potential medical plants. The destruction of the Amazon Rainforest is not a price worth paying for Brazil's economic development because it is putting the earth in danger too. The only way to slow down the results deforestation is by sustainable methods such as planting back the trees and teaching people the importance of foliage.

Wednesday, October 23, 2019

Comparing and Contrasting Sonnet 130 and Ars Poetica Essay

â€Å"Change what you see by changing how you see† (Huie). This quote relates to â€Å"Sonnet 130,† by William Shakespeare and â€Å"Ars Poetica,† by Archibald Mac Leish. Sonnet 130 is about the faults of his mistress, but realizes by the end of the poem, that his love is all that matters. This man did not see his mistress as an ugly woman, but instead saw her as someone whom he loves dearly. In a different way, Ars Poetica states that â€Å"a poem should not mean, but be† (MacLeish 23). People who read a poem may try to interpret its real meaning, but there is really nothing to interpret. A poem should just mean what it says. Although both â€Å"Sonnet 130,† by William Shakespeare and â€Å"Ars Poetica,†by Archibald MacLeish have similar themes such as simplicity, and similar devices such as using imagery to describe beauty and nature, they have different meanings, since one poem seems to expect a considerable amount from a mistress, and the other poem expects nothing of a poem. One similarity between â€Å"Sonnet 130† and â€Å"Ars Poetica† is their themes of wanting nothing but simplicity in a poem and a mistress (stated in the last couplet), and love and adoration. When reading â€Å"Sonnet 130† one might think that this man spends his time complaining about his mistress, and clearly dosen’t love her, however, by the end of the poem he realizes that his mistress may not be beautiful, but their love is beautiful, and that is all that matters. â€Å"And yet, by heaven, I think my love as rare† (Shakespeare 13). In â€Å"Ars Poetica,† MacLeish explains that â€Å"a poem should be wordless† (7) and â€Å"a poem should be motionless in time† (9). One might be confused by what the poem is actually trying to say, but he ended the poem by saying, â€Å"a poem should not mean, but be† (23), which was a clearer statement. As was said before, a poem is not something people should over analyze, it should just make you feel the way it does. Almost ike a painting or sculpture, a poem is not a puzzle, but a mood or a feeling. Both poems seem to have different views on what to expect from a mistress/poem. In â€Å"Sonnet 130,† Shakespeare expects a great deal of things from his mistress. There are twelve lines discussing the disappointment of his mistress’ eyes, lips, hair, cheeks, breath, voice, and how she walks. Lines such as her eyes â€Å"are nothing like the sun† (Shakespeare 1), her lips are less red than coral, and her hairs are like black wires growing on her head, show how displeased he is at these unattractive qualities. â€Å"Ars Poetica† is completely different in this way. MacLeish says, â€Å"A poem should be palpable and mute† (1), and â€Å"Dumb as old medallions to the thumb† (3). These words demonstrate how he believes that poetry should be different than what society expects them to be. He wants nothing of a poem, but just believes that poems should be whatever they want to be. Another similarity between â€Å"Sonnet 130† and â€Å"Ars Poetica† would be that they both use imagery to compare beauty and nature. â€Å"Sonnet 130† used this device, to demonstrate the nature of beauty through imagery. â€Å"I have seen roses damask’d, red and white, but no such roses see I in her cheeks† (Shakespeare 5). This compares his mistress’ cheeks to the beauty of a rose. â€Å"Ars Poetica† has many lines that use imagery, one of which compares words to the flight of birds, â€Å"a poem should be wordless as the flight of birds† (MacLeish 7). Both writers did an impeccable job using imagery to enhance the readers understanding and use descriptive words to make the poem more beautiful and interesting sounding. â€Å"Ars Poetica† and â€Å"Sonnet 130† are similar in the way that they both have a similar theme of simplicity and adoration. â€Å"Ars Poetica† wanting a poem be in it’s simplest terms and wanting it to mean only just what it says. Although in â€Å"Sonnet 130† Shakespeare does seem to expect a lot from a mistress, he states at the end of the poem, that he wants nothing more than the mistress he has. Another similarity is that they both compare beauty and nature. This device was used purely to entice the reader, and make it easier for the reader to understand. One essential difference between both poems, would be that in â€Å"Ars Poetica, the poet strongly believes that a poem should be â€Å"wordless† and simple, almost careless. However, in â€Å"Sonnet 130,† Shakespeare spends most of the poem taking about his mistress’ unattractive qualities and seems quite expectant of a number of things. Analyzing these key similarities and differences are what help the reader understand the poem in a more analyitical way.