CUL Ethical Dilemma in The Covid 19 Vaccination Essay Writing Assignment Help

I’m working on a science presentation and need support to help me understand better.

Eagle Aerospace Industry, taking examples from other aviation and aerospace industries, aims to develop a CSR within its organizational system.

Of course, in people, culture is learned not inherited. It derives from one’s social environment, not from one’s genes. Culture should be distinguished from human nature on one side and from an individual’s personality on the other, although exactly where the borders lie between human nature and culture, and between culture and personality, is a matter of discussion among social scientists.

On the other hand, organizational culture is a system of shared assumptions, values, and beliefs, which governs how people behave in organizations. These shared values have a strong influence on the people in the organization and dictate how they dress, act, and perform their jobs.

Every organization develops and maintains a unique culture, which provides guidelines and boundaries for the behavior of the members of the organization.

Under these two concepts and after you have read the material provided for corporate social responsibility:

Identify the key elements to develop a CSR culture in Eagles Aerospace Industry.

You must consider the distinct characteristics of aerospace industries, what benefits CSR culture could bring to the internal and external social environment, and what types of actions/operations are necessary to develop and support a CSR culture.

Your plan for a CSR culture should be presented in a narrated presentation of about 4-6 minutes.

I’m working on a writing case study and need a sample draft to help me learn.

Read “Application Case 5.3: Mining for Lies,” located in Chapter 5 of the textbook.

In 50-100 words each, address the questions presented at the end of the case study.

Driven by advancements in Web-based information technologies and increasing globalization, computer- mediated communication continues to filter into everyday life, bringing with it new venues for decep-tion. The volume of text-based chat, instant messag-ing, text messaging, and text generated by online communities of practice is increasing rapidly. Even e-mail continues to grow in use. With the massive growth of text-based communication, the potential for people to deceive others through computer-mediated communication has also grown, and such deception can have disastrous results.Unfortunately, in general, humans tend to perform poorly at deception-detection tasks. This phenomenon is exacerbated in text-based commu-nications. A large part of the research on deception detection (also known as credibility assessment) has involved face-to-face meetings and interviews. Yet, with the growth of text-based communication, text-based deception-detection techniques are essential.Techniques for successfully detecting decep-tion—that is, lies—have wide applicability. Law enforcement can use decision support tools and techniques to investigate crimes, conduct security screening in airports, and monitor communications of suspected terrorists. Human resources profession-als might use deception-detection tools to screen applicants. These tools and techniques also have the potential to screen e-mails to uncover fraud or other wrongdoings committed by corporate officers. Although some people believe that they can readily identify those who are not being truthful, a summary of deception research showed that, on average, peo-ple are only 54% accurate in making veracity deter-minations (Bond & DePaulo, 2006). This figure may actually be worse when humans try to detect decep-tion in text.

Using a combination of text mining and data mining techniques, Fuller et al. (2008) analyzed person-of-interest statements completed by people involved in crimes on military bases. In these state-ments, suspects and witnesses are required to write their recollection of the event in their own words. Military law enforcement personnel searched archival data for statements that they could conclusively iden-tify as being truthful or deceptive. These decisions were made on the basis of corroborating evidence and case resolution. Once labeled as truthful or deceptive, the law enforcement personnel removed identifying information and gave the statements to the research team. In total, 371 usable statements were received for analysis. The text-based decep-tion-detection method used by Fuller et al. (2008) was based on a process known as message feature mining, which relies on elements of data and text mining techniques. A simplified depiction of the process is provided in Figure 5.3.First, the researchers prepared the data for pro-cessing. The original handwritten statements had to be transcribed into a word processing file. Second, features (i.e., cues) were identified. The researchers identified 31 features representing categories or types of language that are relatively independent of the text content and that can be readily analyzed by auto-mated means. For example, first-person pronouns such as I or me can be identified without analysis of the surrounding text. Table 5.1 lists the categories and an example list of features used in this study.The features were extracted from the textual statements and input into a flat file for further pro-cessing. Using several feature-selection methods along with 10-fold cross-validation, the researchers com-pared the prediction accuracy of three popular data mining methods. Their results indicated that neural network models performed the best, with 73.46% pre-diction accuracy on test data samples; decision trees performed second best, with 71.60% accuracy; and logistic regression was last, with 65.28% accuracy.The results indicate that automated text-based deception detection has the potential to aid those who must try to detect lies in text and can be suc-cessfully applied to real-world data. The accuracy of these techniques exceeded the accuracy of most other deception-detection techniques, even though it was limited to textual cues.

Questions for Discussion

1. Why is it difficult to detect deception?

2. How can text/data mining be used to detect deception in text?

3. What do you think are the main challenges for such an automated system?

I’m working on a health & medical multi-part question and need support to help me learn.

Wk 2 Individual – The Importance of Accountability Paper

Assignment Content

• Answer the first question
  Pick 2 of the following questions (in addition to the first question) below and answer them in a question/answer format.
  (You must answer a total of 3 questions)
  Write a 200-300 answer for each question chosen.
  • Why is accountability important in the health care industry? (Mandatory)
  • How is an employee’s accountability measured in the health care industry?
  • How does accountability apply to ethical considerations in leadership and management?
  • What does a checks-and-balances process look like in a successful healthcare organization?
  • ( non-financial checks and balances)
  • How does lack of accountability affect an organization’s working culture?
  • How can you maintain a positive working culture and avoid a culture of blame?
• Cite at least 3 sources (1 source can be your textbook)
  Follow APA guidelines for citing and references, title page is not necessary.
  Format your paper and sources according to APA guidelines.
• Submit your assignment.
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  • Center for Writing Excellence
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• Copyright 2019 by University of Phoenix. All rights reserved.

I’m working on a writing case study and need a sample draft to help me understand better.

Read “Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World,” located in Chapter 5 of the textbook.

In 50-100 words each, address the questions presented at the end of the case study.

Over the past 10 years, the cable television sector in the United States has enjoyed a period of growth that has enabled unprecedented creativity in the creation of high-quality content. AMC Networks has been at the forefront of this new golden age of tel-evision, producing a string of successful, critically acclaimed shows such as Breaking Bad, Mad Men, and The Walking Dead.Dedicated to producing quality programming and movie content for more than 30 years, AMC Networks Inc. owns and operates several of the most popular and award-winning brands in cable televi-sion, producing and delivering distinctive, compel-ling, and culturally relevant content that engages audiences across multiple platforms.Getting Ahead of the GameDespite its success, the company has no plans to rest on its laurels. As Vitaly Tsivin, SVP Business Intelligence, explains: “We have no interest in stand-ing still. Although a large percentage of our business is still linear cable TV, we need to appeal to a new generation of millennials who consume content in very different ways.“TV has evolved into a multichannel, mul-tistream business, and cable networks need to get smarter about how they market to and con-nect with audiences across all of those streams. Relying on traditional ratings data and third-party analytics providers is going to be a losing strat-egy: you need to take ownership of your data, and use it to get a richer picture of who your viewers are, what they want, and how you can keep their attention in an increasingly crowded entertainment marketplace.”

Zoning in on the ViewerThe challenge is that there is just so much information available—hundreds of billions of rows of data from industry data providers such as Nielsen and com-Score, from channels such as AMC’s TV Everywhere live Web streaming and video on-demand service, from retail partners such as iTunes and Amazon, and from third-party online video services such as Netflix and Hulu.“We can’t rely on high-level summaries; we need to be able to analyze both structured and unstructured data, minute-by-minute and viewer-by-viewer,” says Vitaly Tsivin. “We need to know who’s watching and why—and we need to know it quickly so that we can decide, for example, whether to run an ad or a promo in a particular slot during tomor-row night’s episode of Mad Men.”AMC decided it needed to develop an industry-leading analytics capability in-house—and focused on delivering this capability as quickly as possible. Instead of conducting a prolonged and expensive ven-dor and product selection process, AMC decided to leverage its existing relationship with IBM as its trusted strategic technology partner. The time and money traditionally spent on procurement were instead invested in realizing the solution—accelerating AMC’s progress on its analytics roadmap by at least 6 months.Empowering the Research DepartmentIn the past, AMC’s research team spent a large por-tion of its time processing data. Today, thanks to its new analytics tools, it is able to focus most of its energy on gaining actionable insights.“By investing in big data analytics technology from IBM, we’ve been able to increase the pace and detail of our research an order of magnitude,” says Vitaly Tsivin. “Analyses that used to take days and weeks are now possible in minutes, or even seconds.“Bringing analytics in-house will provide major ongoing cost-savings. Instead of paying hundreds of thousands of dollars to external vendors when we need some analysis done, we can do it ourselves—more quickly, more accurately, and much more cost-effectively.

We’re expecting to see a rapid return on investment.“As more sources of potential insight become available and analytics becomes more strategic to the business, an in-house approach is really the only viable way forward for any network that truly wants to gain competitive advantage from its data.”Driving Decisions with DataMany of the results delivered by this new analyt-ics capability demonstrate a real transformation in the way AMC operates. For example, the company’s business intelligence department has been able to create sophisticated statistical models that help the company refine its marketing strategies and make smarter decisions about how intensively it should promote each show.InstrumentedAMC combines ratings data with viewer information from a wide range of digital channels: its own video on-demand and live-streaming services, retailers, and online TV services.InterconnectedA powerful and comprehensive big data and analytics engine centralizes the data and makes it available to a range of descrip-tive and predictive analytics tools for accelerated modeling, report-ing, and analysis.

IntelligentAMC can predict which shows will be successful, how it should schedule them, what promos it should create, and to whom it should market them—helping to win new audience share in an increasingly competitive market.With deeper insight into viewership, AMC’s direct marketing campaigns are also much more successful. In one recent example, intelligent segmentation and lookalike modeling helped the company target new and existing viewers so effectively that AMC video on-demand trans-actions were higher than would be expected otherwise.This newfound ability to reach out to new viewers based on their individual needs and pref-erences is not just valuable for AMC—it also has huge potential value for the company’s advertising partners. AMC is currently working on providing access to its rich data sets and analytics tools as a service for advertisers, helping them fine-tune their campaigns to appeal to ever-larger audiences across both linear and digital channels.Vitaly Tsivin concludes: “Now that we can really harness the value of big data, we can build a much more attractive proposition for both consum-ers and advertisers—creating even better content, marketing it more effectively, and helping it reach a wider audience by taking full advantage of our multichannel capabilities.”

Questions for Discussion

1. What are the common challenges broadcasting companies are facing nowadays? How can ana-lytics help to alleviate these challenges?

2. How did AMC leverage analytics to enhance their business performance?

3. What were the types of text analytics and text mini solutions developed by AMC networks? Can you think of other potential uses of text mining applications in the broadcasting industry?