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What is Journal?

A journal, in academic contexts, refers to a peer-reviewed publication in which researchers present original studies, reviews, and analyses across virtually every field of inquiry. Students encounter journal articles in courses ranging from nursing and public health to ethics, education, history, and social sciences. Working with journals teaches critical reading skills, because published research demands that readers evaluate methodology, assess the credibility of findings, and understand how authors position their arguments within broader scholarly conversations. The ability to locate, interpret, and respond to journal sources is foundational to undergraduate and graduate academic work.

The papers collected here reflect a wide range of approaches to engaging with journal sources. Many take a review or synthesis format, summarizing findings and implications from multiple articles on topics such as bilingual education, high school dropout rates among Native Americans, father absence and adolescent drug use, and oral health. Others focus on a single article or study, analyzing how researchers frame their data and what their conclusions support. Some papers extend into annotated bibliography form, evaluating sources on subjects like race, class, gender, and ethical issues in business management, while others connect journal research to professional practice contexts such as nursing or school counseling.

A strong essay engaging with journal literature requires a focused thesis that moves beyond summary toward analysis or argument — explaining not just what researchers found, but why those findings matter or where they fall short. Evidence drawn directly from the article's data, methodology, and stated implications carries the most weight. The most common pitfall is treating a journal article as simply true rather than as a constructed argument subject to scrutiny.

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Essay Doctorate
Origin of simian immunodeficiency virus in wild western gorillas
Phylogenetic Analysis of SIV in Western Gorillas
Essay Doctorate
Analytics and the Growing Dominance of Big
The level of uncertainty and risk that pervade many enterprises today is growing, as the dynamics and economics of markets are changing rapidly. The many rapid, turbulent structural changes in industries is also leading to a greater reliance on analytics and the nascent area of Big Data as well. The potential of this second area, Big Data, is in determining patterns in massive data sets that have in many cases been collected for decades within enterprises. The abundance of data within enterprises, when combined with Big Data aggregation and analytics techniques, can be used for drastically reducing risk and uncertainty in even the most challenging and fast-moving industries. Big Data is being hyped heavily by analytics systems and enterprise application providers as well, as this category of software allows for the use of long-standing analytics and business intelligence (BI) tools expanded supporting larger data sets. Many companies today are working to create enterprise-wide platforms for managing massive data sets, many of them integrating legacy and 3rd aprty databases many of which have never been integrated into a broader platform strategy before (Jacobs, 2009). These larger data sets and their inherent complexity make the overall analysis, aggregation, creation of taxonomies and customizing of reports challenging and difficult to achieve with the baseline or current set of analytics and BI tools available today however. The continual evolution of these applications and the fine-tuning of specific aggregation technologies including Hadoop and Map Reduce (Jacobs, 2009) have also contributed to making Big Data a more strategic foundation fro decision making. Enterprises are facing greater time and cost constraints than ever before, which also leads to the create and continually invest in larger data sets, analytics, BI and advanced reporting technologies all orchestrated to make the most of the terabytes of legacy data companies have (Chisholm, 2009). The rapid development of analytics, BI and data reporting platforms and tools has led to a level of innovation in enterprise software that is making it possible for enterprises to get more insights from the terabytes of data they have been collecting for decades. This category of software tools include analytics, BI, data visualization, product lifecycle data and predictive analytics all orchestrated to create a common platform for reducing risk while bringing greater intelligence into an organization (Ericson, 2010). As is the case with any high growth enterprise software category, there is an abundance of hype surrounding what these analytics and BI platforms and tools are and aren't capable of. The tendency to overlook the very difficult processes to extracting, transferring and loading (ETL) data from legacy systems and creating a highly effective ecosystem of data is very expensive for companies who have never attempted this before. Further, the methodologies needed for consistently and accurately capturing the data within a given enterprise require a level of discipline that many companies are lacking in their core process areas (Jacobs, 2009). Simply put, it is very hard work to capture all the heterogeneous sources of data throughout an enterprise, from the legacy systems to the 3rd party databases, and then perform ETL functions on them in order to create a new system of record for the entire organization to make use of (Ericson, 2010). Yet for organizations to capitalize on the potential that exists from these many diverse forms of information, intelligence and insight throughout their businesses, they must take the time and effort to create a unified, highly integrated single system of record to galvanize their Big Data strategies together (Jacobs, 2009). The objective of this analysis is to provide the arguments for and against having Big Data included in the strategic decision-making process within an enterprise. The strengths are presented first, followed by the weaknesses of this approach to harnessing data throughout an enterprise. The strengths and weaknesses are next compared and an assessment provided. One of the most prevalent technologies used for accomplishing Big Data analytics and intelligence are MapReduce and Hadoop, two aggregation technologies that can compress terabytes of data into taxonomies and quickly analyze them (Jacobs, 2009).
Essay Doctorate
Taiping Carpet Generally, Companies That Ship Products
When it comes to shipping, it is very important to ensure that a company is able to get its goods to its customers quickly. Additionally, those goods need to arrive safely and in good shape. If that is not consistently the case, then the company needs to find a different shipper so that it will not lose money. The issue here is whether a company should contract with a particular shipper or whether it should use any and all available options as necessary.
Essay Doctorate
Pros and cons of Linux open source software for data network services
"Open source secure operating systems are now available, which are compatible with existing software, and hence are attractive for organizations…" (Guttman, 2005). SE Linux offers well thought out security services.
Paper Doctorate
Total quality management and continuous improvement in organizational practice
The concept of "goal translation" in the context of the STM case is critical to the success of the entire TQM initiative and strategy. The single most critical success factor for any TQM initiative is change management…
Essay Doctorate
Journal of Psychoactive Drugs (Reinarman, Et Al.,
¶ … Journal of Psychoactive Drugs (Reinarman, et al., 2011); the authors conducted research into the people / patients that are using legal medical marijuana. The authors assert in the Abstract that while much has been…
Essay Doctorate
Encouraging Seniors to Use the Internet Promotional
Author's note with contact information and more details of collegiate affiliation, etc.
Paper Undergraduate
Life Skills Programs in Nonprofit Organizations
The purpose of this study is to use to use empowerment and social learning theories to evaluate the New Faith Family Center program to determine whether adults with mental illness who are simultaneously homeless could (1) learn life skills in the areas of education/ Career development, employment, budgeting, addiction Recovery, parenting skills/ anger management, Health Care and child care and (2) retain their knowledge and skills three to six months after completing the intervention.
Paper Undergraduate
Ethics concepts and frameworks
Several ethical issues emanate from this scenario. I was not informed as the member of the faculty about the main objective behind the issue to be discussed. It did not communicate clearly the sole purpose for the issue discussed. Ethically, the head of the department was wrong. It was worthwhile for us to be enlisted as coauthors or be acknowledged in the reference section. It is also unethical to share patient's information to anyone. It is only under certain exceptional circumstances that would demand that I share confidential information entrusted to me by the client during the therapy.
Research Paper Doctorate
Implementing lean operations in manufacturing and service industries
The theory of constraints, which was created by Elivahu M. Goldratt, is a particular body of knowledge that addresses effective management of various organizations as systems (McMullen, 1998).