Iron sharpens iron, and one man sharpens another Essays

"Iron sharpens iron, and one man sharpens another" Proverbs 27:17 (ESV) This verse speaks so much to my life because of experiences I have been through with friendships. What I have learned about friendships is that they can either build or break you. You need good friends, and you need to be a good friend. Iron can sharpen iron, and a good friend can sharpen a friend. The iron of a file can make a blade sharper, and a good friend can make his friend better. A knife is not sharpened by cloth, bread, wood, plastic, or even gold. A knife may cut and shape these things for them to be more useful, but these things will only dull the knife. Sharpening a knife requires iron or a substitute for iron at least as hard as the knife. Once sharpened, a knife is much more productive with less effort on such things. This proverb is about good friends - they will make you better. A good friend will make you brighter, sharper, and more useful. But not any friend will do, only wise friends make you wiser. Foolish and bad friends will darken and corrupt your life. I believe this verse is very important for us as college students because we are building our future and if we surround ourselves with bad influences we will also be influenced by bad habits. My pastor back at home use to say "show me who you hang around with and I will tell you who you are."

College Sample Essay Questions For Entrance Examination

College Sample Essay Questions For Entrance ExaminationCollege sample essay questions for entrance examination are an important and effective method to prepare for the entrance exam. There are many students who have trouble with essay writing skills. In this case, writing an essay is an integral part of their application. It helps them communicate their ideas and thoughts.College sample essay questions for entrance examination are a great way to test your knowledge of the subjects on the entrance exam. These are the factors that matter to colleges. The grade reflects on how well you can write. This is one reason why colleges want to see how well you write. Asking question for entry to determine how well you can write in terms of college sample essay questions for entrance examination will not be an easy task.Choosing a topic is the first step in essay writing for the entrance exam. If your subject is psychology or philosophy, you can go with other humanities and social sciences subje cts. If you like to be adventurous, there are arts subjects such as art history, theater, and music that would be good for you. The main point is that you choose a topic that you think you would like to write about if you went to college.When you are choosing a topic for the college entrance exam, there are many ways to do it. You can go online to find topics, read sample essays or books on these topics and study them. Doing so will give you a feel for what is important in writing an essay. This will help you figure out what kinds of topics interest you. You can then think of questions for the exam and then write out the solution.Another thing that you need to take into consideration when choosing a topic for your college entrance exam is the length of the essay. Collegeslook at the length of the essay, because there are some essay topics that can last for several pages. This means that it might be hard to fit in the required number of sentences. You should make sure that you are ab le to incorporate a lot of information into your essay and be able to make a compelling argument in it. Making the required number of sentences and being able to make a compelling argument is a factor that college test writers look at before they choose the topic of your essay.It is best to find a topic that is related to your major so that you can avoid boring essays. It is also best to stick to one topic so that you can develop a stronger understanding of the topics that are being studied. Some of the topics that you may not be familiar with may still be necessary topics for the college entrance exam. Even if you are not familiar with them, you can ask someone who is to give you the best information possible. A person who knows what they are talking about is always the best.Another way to find the right topic for your college entrance exam is to be creative and take your own ideas. You can get ideas from others in your classes. Your instructor is always happy to help you with rese arch or problems.College entrance exam questions for essay writing should be your main concern when writing your essay. It is important to remember that essay writing is the main objective of your application. Having an idea of what the admission requirements of the college you are applying to are will help you in your selection of a topic for your essay.

Prueba de ADN para la tarjeta de residencia permanente

Prueba de ADN para la tarjeta de residencia permanente Durante los trmites para obtener la tarjeta de residencia por peticià ³n de un familiar es posible que se exija  una prueba de ADN para probar que efectivamente existe un và ­nculo de sangre entre la persona que pide los papeles y el potencial migrante para el que se solicitan. Tanto los ciudadanos americanos como los residentes permanentes legales pueden solicitar tarjeta de residencia, tambià ©n conocida por green card, para diferentes familiares pero el gobierno quiere estar seguro de que esa relacià ³n es verdadera. Por ello es importante conocer por quà © se puede pedir un test de ADN, si es necesario o voluntario o, en este à ºltimo caso, si es recomendable tomarlo y, finalmente, cà ³mo es la tramitacià ³n de todo el proceso.  ¿Por quà © se puede pedir una prueba de ADN en las peticiones de familia? Las pruebas de ADN sirven para probar genà ©ticamente la relacià ³n biolà ³gica entre dos personas en la tramitacià ³n de las visas de familia.   Por ejemplo, que entre solicitante y pedido hay efectivamente una relacià ³n de padre e hijo, madre e hijo, hermanos o hermanastros, etc. Para probar esta relacià ³n es siempre necesario contar con ejemplos biolà ³gicos de las dos personas cuya relacià ³n familiar se trata de establecer. Generalmente basta con pasar un bastoncillo por el interior de la boca.  ¿Es obligatoria la prueba de ADN en todas las peticiones por familia biolà ³gica? No, y de hecho son la excepcià ³n. Generalmente se prueba la relacià ³n entre solicitante y pedido ms all de toda duda, mediante documentos, como por ejemplo, el certificado o acta de nacimiento. Como son pruebas caras y que lleva tiempo practicarlas sà ³lo se solicitan estas pruebas cuando no existen otros medios para demostrar la relacià ³n entre dos personas, como pueden ser documentos o fotografà ­as, o por la razà ³n que sea se sospecha de que el và ­nculo biolà ³gico puede no ser real.   Es tambià ©n ms comà ºn que este tipo de prueba se solicite cuando la tarjeta de residencia se tramita mediante un procedimiento consular que cuando se gestiona todo el proceso dentro de los Estados Unidos mediante un ajuste de estatus. Lo cierto es que es ms importante prestar atencià ³n a otros posibles problemas que pueden surgir en el proceso, como asegurarse de que se cumplen con los requisitos econà ³micos para patrocinar, que se va a pasar el examen mà ©dico al que sà ­ deben de someterse todos los candidatos a migrantes y, finalmente, que no afecta ninguna de las circunstancias que provocan que la tarjeta de residencia sea denegada.   Trmites para la prueba de ADN para la green card cuando se pide Cuando el oficial consular o cualquier funcionario migratorio  solicita una prueba de ADN, el beneficiario de la tarjeta de residencia, si à ©sta se llega a conceder, decide voluntariamente si se somete o no a la prueba. De realizarla, debe cancelar el costo à ©l mismo o el solicitante de la tarjeta (su padre, madre, hijo, hija, hermano o hermanastro) el importe por la prueba, que deber ser abonada al laboratorio antes de realizarse. Solamente pueden realizarse las pruebas de ADN en uno de los laboratorios acreditados por la Asociacià ³n americana de bancos de sangre (AABB, por sus siglas en inglà ©s). Es importantà ­simo verificar que el laboratorio que va a hacer el anlisis est incluido en esa lista. Si no lo est, la Embajada o el consulado americano no admitirn las pruebas y se habr gastado el dinero en vano. Adems, hay que tener en cuenta que hay mucho fraude en este rubro y muchos laboratorios aseguran estar autorizados para realizar estas pruebas para las oficinas consulares americanas y no lo estn. Asà ­ que es muy recomendable tomar el tiempo necesario para checar la lista y una vez que se tenga buscar el laboratorio ms conveniente por su localizacià ³n. El laboratorio enviar el kit directamente a la oficina consular. Jams al solicitante o al beneficiario de la peticià ³n de la tarjeta de residencia. La prueba se har en la propia oficina consular previo pago del arancel correspondiente por los servicios del mà ©dico que har que tomar la muestra. El dà ­a de la cita para este asunto, el beneficiario de la peticià ³n de la visa de inmigrante debe presentarse a la hora fijada con su pasaporte, una foto y el recibo de haber pagado por los servicios mà ©dicos. Una vez que se ha tomado la prueba de ADN, el propio consulado enviar el kit directamente al laboratorio en los Estados Unidos. Y cuando à ©ste obtenga los resultados, se notificarn directamente a la oficina consular. Y una vez que los tenga decidir cancelar el proceso de tramitacià ³n del permiso de residencia o seguir con el mismo. El beneficiario, si asà ­ lo desea, puede solicitar directamente al laboratorio una copia de los resultados. La oficina consular americana nunca otorgar tal copia. A tener en cuenta para tener à ©xito en la peticià ³n de la green card por familia Que la prueba de ADN demuestre que es verdad que el solicitante y el beneficiario son familiares no significa que la peticià ³n de la residencia permanente vaya a ser aprobada. Simplemente quiere decir  que ese requisito ha sido satisfecho. Las peticiones de tarjetas de residencia pueden ser rechazadas por diversas causas. En la mayorà ­a de los casos, si eso sucede asà ­, ser posible pedir un perdà ³n, tambià ©n conocido como  waiver o permiso. Que puede ser o no concedido. Es muy importante en estos casos contar con el asesoramiento de un abogado migratorio con excelente reputacià ³n y con experiencia en este tipo de casos. Tambià ©n es importante, antes de iniciar los trmites, tener una idea aproximada de cunto van a tardar los papeles, ya que en muchos casos la demora es mucho ms grande de lo que se cree. Finalmente, se recomienda tomar este quiz - trivia  o test- para verificar que se tiene los conocimientos bsicos para obtener y conservar la tarjeta de residencia. Es difà ­cil conseguirla. No es conveniente arriesgarse a una denegacià ³n o una cancelacià ³n por falta de informacià ³n. Este es un artà ­culo informativo. No es asesorà ­a legal.

Analysing data production

Analysing data production The process of research is not only about learning and discovering, but also about sharing these discoveries with others, so that society as a whole can benefit from the efforts put in by the individual. When it comes to complex academic concepts, the choice of words for how a concept is described can make a difference to how well it is understood by others , especially when moving between research domains.    Hence we make such use of metaphors and analogies when it comes to describing complex concepts. Tying a concept (for example, quantum superposition) to a real world â€Å"thing† (for example, a cat in a box ) allows people unfamiliar with the original concept to connect it with something they have experience of, and provides a foundation which can be elaborated on. If, upon further examination, it is found that the analogy gets stretched beyond all reason, then that is acceptable, as long as those using it don’t simply rely on it as an article of blind faith. Analogies and metaphors require critical thinking. Scientific concepts are formulated in human language, and as such, are intended to be processed by the human brain (even if that brain needs to be highly trained before it can properly grasp the concepts being described). Scientific data, on the other hand, is designed to be machine consumable (as well as predominantly machine produced). Measurements are often not useful without the context surrounding them. It is one thing to know that a particular river level rose by 10cm. It is only by knowing where this happened, how high the river was to begin with, and how high the rise would have to be at that location to flood the houses built there, that we are able to put the data into context, and make it useful. Yet we still need that data. If a homeowner who got flooded wished to claim on their insurance for flood repairs, having that data and context available means they’d have proof that it was river flooding that caused the damage, rather than a burst pipe. We also need to have the research data which underpins key research findings available and understandable, both for reproducibility and to prevent fraud/misuse. Making data usable by others takes effort and time and is often unrewarded by the current system for gaining academic credit. Metaphors and Analogies â€Å"No one metaphor satisfies enough key data system attributes and that multiple metaphors need to co-exist in support of a healthy data ecosystem†(Parsons Fox, 2013) Data publication as a metaphor has been addressed extensively in (Parsons Fox, 2013), leading to the quote above. But before we dive into examples of metaphor and analogy in the data domain, it is helpful to review what they mean. From (Gentner Jeziorski, 1993): ‘Analogy can he viewed as a kind of highly selective similarity. In processing analogy, people implicitly focus on certain kinds of commonalities and ignore others. Imagine a bright student reading the analogy â€Å"a cell is like a factory.† She is unlikely to decide that cells are buildings made of brick and steel. Instead she might guess that, like a factory, a cell takes in resources to keep itself operating and to generate its products. This focus on common relational abstractions is what makes analogy illuminating.’ (Gentner Jeziorski, 1993) p448 This action of focussing on some commonalities and ignoring others is crucial when using analogies to illustrate scientific concepts. We can produce an analogy that â€Å"a dataset is like a book†. Commonalities include that both contain information, in a structured and formatted way, which is consumable by a user, and both are the product of sustained effort, potentially from a wide range of actors. The differences between them make it just as easy to say â€Å"a dataset is not like a book†, in that a dataset can be constantly changing; may not be a physical, but a virtual object; mostly isn’t designed for humans to read unassisted ; and often a dataset isn’t a self-contained unit (as it requires extra information and metadata to make it understandable and usable). Obviously, it is possible to push analogies too far, and have them break. This is more likely to happen when users of the analogy don’t have a good understanding of each of the two things being compared. In the (Gentner Jeziorski, 1993) quote above, if the student didn’t have any other concept of what a cell was, she could easily imagine that they were tiny buildings made of bricks and steel, and the analogy used would do nothing to correct that misapprehension. It’s also important to remember that analogy is not causation – if two phenomena are analogous, it does not imply that one causes the other. Types of metaphor and real world scientific examples: Data Publication Data publication, as a metaphor, came about as a result of the drive for researchers to publish as many works as possible in as many high impact journals as possible, and the need for those involved in creating datasets to be given recognition for their work, and their efforts to make the data findable, accessible, interoperable and reusable. This resulted in pressure to squeeze all research outputs into shapes that resemble publications, hence the proliferation of the data journal, a place where researchers can publish a paper about their dataset, linked via permanent identifier to the dataset itself (stored in a trustworthy repository). The data paper then can be cited and used as a proxy for the dataset when reporting the importance and impact of the researcher’s work. A real-world example of a dataset that has been published in a data journal is the Global Broadcast Service (GBS) datasets (Callaghan et al., 2013), measurements from a radio propagation dataset investigating how rain and clouds impact signal levels from a geosynchronous satellite beacon at radio frequencies of 20.7 GHz. The data streams linked to the paper, and which the paper describes in detail, are the result of a definite, discrete experiment, resulting in a well-defined, discrete and fully completed dataset, which will not change in the future. The dataset has been through two levels of quality assurance: the first was performed on ingestion into CEDA , where the file formats were standardised and metadata was checked and completed. The second level of quality assurance was performed as part of the scientific peer review process carried out when the data paper and dataset were submitted to the Geoscience Data Journal for review and publication. As this dataset is complete, well-documented and quality assured, it can be considered to be a first-class, reference-able, scientific artefact. There are other peer-reviewed journal articles which use the GBS data as the basis for their results, see for example (Callaghan et al., 2008) . However, datasets can be discrete, complete, well-defined and permanently available without the need for the proxy of a data paper, or any other publication attached to them. This is of particular value when it comes to publishing negative results, or data that don’t support the hypothesis they were collected to verify, but may be useful for testing other hypotheses. These types of datasets are possibly the closest thing we have to the â€Å"dataset as a book† analogy, and therefore are the easiest to fit into the data publication mould. Unfortunately, many other datasets do not fit in with this shape. Many datasets are dynamic, and are modified or added to as time progresses. Then there are issues with granularity – some researchers may only need a subset of a larger dataset for their work, but need to accurately and permanently identify that subset. Citing at the level of every one of the subsets results in reference lists that are long and unwieldy, and can make it difficult to find the subset required in a long list of very similarly named datasets. For text based items, such as books and articles, tools exist to compare text from one instance of an article to another, allowing the reader to be sure that the contents of two instances are the same, regardless of the format they are in (for example, an article in hard copy in a journal as compared with a pdf). We currently do not have a way of evaluating the scientific equivalence of datasets regardless of their format. The ease with which it’s possible to modify datasets (and not track the changes made) also means that it can be very hard to tell which dataset is the canonical, original version, or even what the differences are. Data publication can work very well as a metaphor, but users must be aware that it really is only applicable to the subset of datasets which can be made complete, well-documented, well-defined, discrete and quality controlled. Big Iron (industrialised data production) Big Iron, as defined in (Parsons Fox, 2013) typically deals with massive volumes of data that are relatively homogenous and well defined but highly dynamic and with high throughput. It is an industrialised process, relying on large, sophisticated, well-controlled, technical infrastructures, often requiring supercomputing centres, dedicated networks, substantial budgets, and specialized interfaces. An example of this is the data from the Large Hadron Collider, CERN, but in the Earth Sciences, the Coupled Model Intercomparison Projects (CMIP) are another. The Intergovernmental Panel on Climate Change (IPCC) regularly issues Assessment Reports, detailing the current state of the art of climate models, and their predictions for future climate change. These reports are supported by the data from the climate model runs performed as part of CMIP. Each CMIP is an international collaboration, where climate modelling centres around the world run the same experiments on their different climate models, collect and document the data in standard ways and make it all available for the wider community to use, via custom built web portals. CMIP5, the most recent complete CMIP, resulted in datasets totalling over 2 PB of data. As this data is the foundation for the IPCC assessment and recommendations, it is vital that the data is stored and archived properly . Dealing with these data volumes requires not only custom built infrastructure, but also standards for file and metadata formats (e.g. NetCDF, CF Conventions, CMOR, etc.). Collecting the metadata describing the experiments that were run to create the datasets alone took several weeks’ worth of effort, and several years of effort to design and build the CMIP5 questionnaire which collected the metadata (Guilyardi et al, 2013). The industrialised production of data is likely to increase over the next years, given the increased ability of researchers to create and manage big data. The opposite of this analogy is also valid in many cases, as described in the next section. Artist’s studio (small scale data production, unique and non-standard output) Similar to Big Iron, this analogy focusses on the method of production of a dataset, rather than the dataset itself. The artist studio analogy covers the long tail of data produced by small groups or even single researchers, working in relative isolation. Artist studios generally produce one-of-a-kind pieces, which may have standard shapes and forms (e.g. oil paintings) but may equally come in non-standard shapes, sizes and materials (e.g. sculptures, video and audio installations, performance art etc.) The aim is to produce something of use/interest to a consumer, even if they are part of a limited domain. Similarly, it’s often not easy, or even possible to share the outputs of the studio (it is possible to make copies/prints of paintings, and smaller models of sculptures, but other objects of art, like Damien Hirst’s famous shark in formaldehyde (Hirst, 1991) are nearly impossible to reproduce ). Datasets produced by small research groups follow this analogy. The emphasis is on the production of the finished product, sometimes with the supporting documentation and metadata being neglected, due to lack of time, effort and potentially interest on the part of the creator. If the dataset is only aimed at a small user group, then the metadata is provided as jargon, or users are simply assumed to have a sufficient level of background knowledge. Sharing the data is often not considered, as for the researchers, holding the only copy of the data makes it more valuable, and therefore more likely that they’ll receive extra funding. An example â€Å"artist studio† is the Chilbolton Facility for Atmospheric and Radio Research (CFARR) . It is a small facility, located in Hampshire, UK, with approximately 6 permanent staff, who collectively build, maintain and run a selection of meteorological and radio research instruments. In recent years, the focus of the facility has been on collaborations with other research groups in universities and other research centres. Previously the facility had been more focussed on radio research, and as such had developed its own data format for the instruments it built, rather than tying in with existing community standards. Similarly, the data was stored on a variety of servers, with a bespoke tape backup system. When CFARR’s funding structure changed, pressure was put on the staff to archive all new data and the majority of existing data in CEDA. This made it easier for the facility staff, in that they no longer needed to maintain servers or the backup system, but it made things harder in that effort was needed to convert the data files to netCDF, and to collect and agree on the metadata that should accompany them. The culture change to move from the artist studio model to a more standardised and collaborative model took effort and time, and should not be underestimated. Science Support Science support is what CEDA do on an operational, everyday basis. Even though we’re not directly (or physically) embedded in a research organisation , we interact with researchers and research centres on a regular basis to ensure that the processes for data ingestion are carried out smoothly and efficiently. For data centres embedded in a research centre, data management can be seen as a component of the broader â€Å"science support† infrastructure of the lab or the project, equivalent to facilities management, field logistics, administrative support, systems administration, equipment development, etc. In our case, CEDA concentrates on data management, and providing services to make it and use of data easier for the researcher. Different data centres will have different ways of providing science support to their core user base. For example, an institutional data repository, responsible for all the data being produced by, for example, a university, will have datasets which are non-standardised and are usually geared towards a specific set of intended uses and local reuse in conjunction with other local data. In terms of the â€Å"artist studio† analogy, an institutional repository is like an art gallery or museum, where different datasets will have different data management requirements. By contrast CEDA, which has multiple PB of data in the archives, must standardise in terms of file formats, metadata models etc., hence moving towards a more â€Å"Big Iron† metaphor. In common with institutional repositories, CEDA also focusses on managing data (and sometimes merging datasets to create more useful resources) in order to meet the needs of our user community, which is international in scope and covers a wide range of users, from schoolchildren, to policy makers, to field researchers and theoreticians. Map Making Map making as a metaphor refers to the final representation of the data, and the process of putting the data into a context, primarily geographical. Maps also help to define the boundaries of what is known, and what isn’t. Though data presented in this way tend to be fixed in time, maps are useful for showing dynamical datasets, or time slices through complex multidimensional processes, e.g. the four dimensional structures of clouds/rain changing in time. The results of map making, the maps themselves, are datasets in their own right, and so need to be treated in the same way as other datasets with regard to preservation, metadata etc. The act of plotting some parameter on a geographical map results in a well-standardised structure for intercomparison and visualisation. Linked Data The â€Å"data† in Linked Data are defined extremely broadly and are envisioned as small, independent things with specific names (URIs) interconnected through defined semantic relationships (predicates) using model and language standards (e.g. the Resource Description Framework, RDF). It has a major emphasis on Open Data, as linked data focuses on enabling the interoperability of data and capitalising on the interconnected nature of the Internet. Linked data isn’t commonly used for dealing with scientific data, but instead, is predominantly used in our metadata, where we have complete focus on preservation, curation and quality, unlike other linked datasets available elsewhere. Using linked data for metadata structures does require standardisation and agreement on the formal semantics and ontologies. Linked data is very flexible, and lends itself well to distributed and interdisciplinary connections, provided the formal semantics can be agreed to be applicable across multiple domains. Linked data as a concept unfortunately hasn’t fully permeated the research environment as yet – many scientific researchers don’t understand the semantics (and have little interest in them). Linked data is often used as a support structure for Big Iron. The Cloud: â€Å"x as a service† There is an argument that the mechanisms for data publication should be invisible, and data should be accessible and understandable without any prior knowledge. Cloud services such as Dropbox allow users to store their data, and access them from any web browser, or mobile app, provided they have an internet connection. â€Å"Data as a service† ties in with â€Å"software as a service†, in that the users only take the data they need at any given moment, and in some cases may not even download it, instead using dedicated computing resources elsewhere to perform the manipulations needed on the data. An example of this is JASMIN , a system that provides petascale storage and cloud computing for big data challenges in environmental science. JASMIN provides flexible data access to users, allowing them to collaborate in self-managing group workspaces. JASMIN brings compute and data together to enable models and algorithms to be evaluated alongside curated archive data, and for data to be shared and evaluated before being deposited in the permanent archive. Data, in this context, aren’t the fixed and complete products described in other analogies, but instead are more fluid and dynamic. Still, once the datasets are deposited in the permanent archive, they become fixed products, and are citeable and publishable. Providing significant resources for data manipulation is undoubtedly useful, but the focus with this system is on the service, not necessarily on the data. The data however, is the backbone of the system – there is no point having the service without the data and the users who want to analyse it. Conclusions It goes without saying that all analogies are wrong, but some are useful, and hence should come with a health warning – especially when following an analogy to the furthest reaches of its logic can result in sheer absurdity . When dealing with data, just like in life, there is no all-encompassing metaphor for what we do. Instead, metaphors and analogies should be used in ways to illuminate and clarify, but we should always remember that metaphors are useful tools for thinking about things, but can also limit how we think about things. (Ball, 2011). Pushing an analogy so far that it breaks can be a useful process, in that it helps determine the limits of understanding, especially as part of an ongoing conversation. Finally, for this essay, the author would like to leave the reader with some very appropriate words from (Polya, 1954, page 15): â€Å"And remember, do not neglect vague analogies. But if you wish them respectable, try to clarify them.†

Analysis of the movie October Sky in terms of socialization Essay

Analysis of the movie October Sky in terms of socialization - Essay Example He is unable to 'think big' in the context of intellectual freedom and the procedure is just the extension of the local neighborhood, or in other words, the coal mines. As we have seen in the novel "Germinal" by Emil Zola, the local characters other than Homer tends to be engrossed by the day to day living and livelihood of the mines and are just unable to think beyond the parameters of their circumstances. "That is simply what happens to kids in Coalwood, at least those who can't escape by means of a football scholarship. They become coal miners, fighting to make a living, threatening to strike, and choking on coal dust." (Chastain, Norman Transcript) In this scenario, Homer finds himself in a situation where he finds no alternative other than rockets to opt for a better living. He tries to influence other kids to help him build rockets. The two other teens who agreed to help him out in this matter appears to be no as foresighted as Homer and are generally reluctant with a tint of amusement included to it. But all is not that muted after all. As in our social life, Homer finds a supportive hand in his mother and his school teacher.

Importance of Enlightenment to colonial history Essay

Importance of Enlightenment to colonial history - Essay Example Enlightenment principles contradicted colonial practices and were very instrumental in ending colonization. In the nineteenth and eighteenth centuries, the legality of colonialism was a subject of argument among the British, French and German philosophers. Key enlightenment thinkers including Diderot, Kant and Smith challenged the notion that it was the responsibility of the Europeans to civilize the world and criticized the cruelty of colonialism. They further insisted that every person had the ability to reason and therefore capable of own government. As far as they were concerned, colonial supremacy was unethical because it entailed expropriation of belongings, forced labor and slavery all of which were against the principles of self governance. According to Diderot, a critic of European colonization, the idea that the colonized individuals gained as a result of civilization by the Europeans was absolutely mistaken and instead the uncivilized lot was the European colonists. He further opposed colonization by arguing that culture enhanced customs of respect and boosted morality in an individual. However, these norms have a propensity of being undermined when a person is far away from his nation of origin. Additionally, he supposed that in most cases, the colonial empires became the places of severe cruelty since the colonists were distant from the informal sanctions and legal institutions which made them not to exercise restraints, instead demonstrate man’s brutal nature at its worst. Some of the proponents of colonization in the seventeenth and sixteenth century, like the Spanish philosophers, wrongly justified colonization by arguing that it was a vital and necessary factor in the realization of the right to commerce. However, Diderot refuted this approach by stating that it was not right for the explorers and foreign traders to access already occupied lands. On the contrary, he noted that only the areas that had no human settlements were fit for

black orpheus Essay -- essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  Love and Death in Black Orpheus   Ã‚  Ã‚  Ã‚  Ã‚  In fiction or reality being overly ambitious can cause one to yield to the evils of temptations. In Black Orpheus the myth fits into the story because it demonstrates the extremes an individual will endure to regain lost love, and relive the past. In the movie Orpheus and Eurydice both experience a case of â€Å"love at first at first sight†. They barely know each other but feel that because of Greek Mythology they were destined to love each other. When Orpheus asked Eurydice her name, and she responded he said that he knew he loved her. Another factor of love is displayed when Orpheus leaves Mira his fiancà © for Eurydice, someone who he barely knew.   Ã‚  Ã‚  Ã‚  Ã‚  In the myth of Orpheus and Eurydice, Orpheus and Eurydice become married; however these two gain unconditional love for each other like in the movie. Orpheus loves Eurydice with all his heart, and would do anything to savage their relationship. Orpheus feels as if nothing could harm them, not even death. In Black Orpheus the two of them also conquer jealousy from the town’s people. Many people didn’t encourage the love these two had built and basically wanted the love affair between them two to end.   Ã‚  Ã‚  Ã‚  Ã‚  In the movie Orpheus tried to protect Eurydice from anything he felt would harm her, including his fiancà © Mira, who evidentially hated her. Mira tried on numerous occasions to harm Eurydice, but Orpheus would always be their as a form of protection. Orpheus also tries to protect Eurydice from the skeleton man who symbolized â€Å"death†. Orpheus goes beyond the call of duty to comfort Eurydice because she was very frightened by his appearance. Orpheus followed Eurydice around when she became startled.   Ã‚  Ã‚  Ã‚  Ã‚  When the skeleton man finally succeeds at harming Eurydice Orpheus tries to save her by running to her rescue. However the only thing that was saved was his feelings for Eurydice and his memory. Death is represented when Orpheus electrocutes Eurydice on the cables. In the myth Orpheus tries to come to her aid several times because of the unconditional love he had for her. Once again, like in the movie â€Å"death† comes for Eurydice again. Unlike the movie, she was killed by snake bites which lead to the end of her. .. ...ause him to walk off the cliff with Eurydice in his arms, and he dies. In the myth of Orpheus and Eurydice Orpheus encounters death when the women tried to kill him by throwing a javelin and some stones. These weapons did not prevail against him because of the love he obtained for music. Death also became prominent in the myth when the women began to scream which drowned out his music and caused his death. Mainly because of Orpheus’s crave for love and his ambitious nature, he leads himself to death. When Orpheus dies, he and his long lost love are reunited once more. In the myth it is said now that they roam happy in the fields together now, sometimes he leading, sometimes she; Orpheus gazes as much as he will upon her, no longer incurring a penalty for a thoughtless glance. This Myth stresses the importance of love and moreover trusts. If Orpheus only trusted Eurydice from the start he would not have been put in that situation. This story and myth also demonstrates the extremes an individual will endure to regain lost love, and relive the past. In this World, happiness is the most important element through success. Happiness can be created through love and hard dedication.