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Nursing Rhetoric
Student Name
Department of Rhetoric & Writing Studies, San Diego State University
RWS 305W
Professor Centanni
19 February 2020
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Nursing Rhetoric
The Greek philosopher Plato once said that “rhetoric is the art of ruling the minds of
men.” Rhetoric is meant to persuade the audience and its effectiveness depends heavily on how
the information is delivered. Certain fields of study rely on different strategies to convince their
audience. In the medical field, authors often use statistics, refer to credible sources and concede
the flaws of an argument in order to appear reliable and authoritative. In both MacWilliams et
al.'s (2013) “Men in Nursing” and Koukourikos et al.'s (2019) “Benefits of Animal Assisted
Therapy in Mental Health," medical scholars utilize multiple rhetorical strategies to appeal to the
audience's senses of logic, emotion, and character. In doing so, they are able to strengthen their
stance and ultimately persuade the readers.
The target audience for articles in this field is typically those in the medical field, as
evidenced by the fact that it requires a background knowledge of certain medical practices and
diseases to fully grasp them. For example, Koukourikos et al. (2019) thoroughly explain how
animal assisted therapy helps patients with depression, autism, dementia and schizophrenia, but
they don’t define these diseases and they use many medical terms that those not in the medical
field would not typically know (p. 1900). A certain level of medical knowledge is also necessary
to understand the context of MacWilliams et al's text, too, because it utilizes different types of
evidence to show the disparity between men and women in nursing. The article is supported by
different quantitative and qualitative studies that identify the causes of the gender inequality in
nursing, which can be unfamiliar to the general public (MacWilliams et al., 2013, p. 39). The
article “Men in Nursing” is also for readers who are interested in gender diversity in the
workplace.
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Since the audience is so intimately aware of the medical field, one strategy that allows
scholars to portray appropriate decorum is following the scientific method. Both articles follow
this time-tested format. The organization of these texts is familiar to those in research and in the
medical field, as each piece has an introduction, objective, methodology, results, and conclusion
section (MacWilliams et al., 2013; Koukourikos et al., 2019). This, alone, shows reverence for
the expectations of the scientific community, but to further their ethos appeal, the authors also
explain how they found their credible sources in their methodology sections. In both articles, the
evidence for their arguments was found with the use of reputable databases, which gives the
authors more credibility and appeals to an audience’s sense of ethos. MacWilliams et al. (2013)
chose their articles using “Education Resources Information Center (ERIC) and the Cumulative
Index to Nursing and Allied Health Literature (CINAHL) electric databases” (p. 39). These are
highly reputable, well-known databases that all medical and nursing professionals will recognize,
This allows the information to come from a place of authority rather than requiring readers to
check the research themselves. With the use of these strategies, the audience is more likely to be
persuaded because the authors demonstrated their expertise on the information and presented it
in the expected format and with credible sources.
Credibility in science extends beyond the format and into the values of perpetual
curiosity, so by acknowledging the limitations or flaws in their work these scholars actually
strengthen their ethos. One example of this is when Koukourikos et al. (2019) assert that
although animal therapy seems to achieve positive outcomes, “research into the involvement of
animals in the treatment of mental illness needs to be broadened and enriched” because there are
“obvious weaknesses and constraints” (p. 1903). In other words, while success looks like a likely
result of these methods, Koukourikos et al. ensure their readers that more testing needs to be
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done to confirm the hypotheses. This is scientific thinking at its core: the notion that the only
way to confirm a truth is through replication. Scholars do not admit these limitations to degrade
their work, but rather to suggest that they would prefer multiple studies find the exact same result
before trusting the methods as fact. This makes the researchers appear more virtuous and honest,
and the readers are more likely to trust them. This honesty is a quality that most audiences value
and appreciate, but particularly in the field of science, it shows the reasonable understanding that
something must be true on repeated occasions before it can be truly trusted - a core value of the
medical and nursing field.
Case studies and statistics are also common strategies in research articles because they
can appeal to an audience’s logic. In “Men in Nursing”, the authors mention that “men still
represent fewer than 10% of the RNs licensed since 2000 and fewer than 12% of the students
enrolled in baccalaureate nursing programs” (MacWilliams et al., 2013, p. 38). These statistics
are factual and difficult to dispute because they are objective figures, which appeals to a reader’s
logic. By listing these statistics, the authors are able to emphasize how there is a lack of male
nurses in the field and in school. The nursing profession often relies on statistics and facts as
evidence to support an action or new practice. The same article also shares a case study where
male nursing students from a public university described “a diminishing population of male
students as they progressed through the nursing program” (MacWilliams et al., 2013, p. 40).
Case studies are also used in nursing research because it provides detailed information and
insight for further research. Nursing is a profession centered around evidence-based practice,
meaning that any action or decision making is supported by various types of evidence, including
experiments, statistics or case studies.
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While most medical fields prefer the aforementioned ethos and logos appeals, audiences
can still be susceptible to emotions, which is why the field of nursing does dabble in charged
language at times. An illustrative example of this is how Koukourikos et al. (2019) refer to
“patients suffering from mental illness" who "often feel powerless, vulnerable and dependent on
other people” (p. 1900). Had the scholars only noted the feeling of dependence, a reader could
infer that patients in this description are more likely to feel a particular way emotionally.
However, their choice to include not one, but three words that have sad connotations "suffering," "powerful," and "vulnerable" (Koukourikos et al., 2019, p. 38) - invite the reader to
feel a sense of concern rather than merely a measured decision to act on behalf of patients.
Readers in this field would likely be drawn in by the hard stats alone, but this appeal can make
them feel a sense of ethical responsibility, as well. Ethical responsibility is a defining factor of
the medical field, as each doctor has to swear the Hippocratic Oath, so this strategy is likely to
work quite well in persuading readers. By finding common ground in sympathy, the author is
able to introduce the argument, which the audience will be more likely to listen too because they
sympathize with the subject.
The use of various rhetoric strategies can be effective in conveying an argument. The
most effective argument connects to the readers’ heart, brain, and "gut." In the medical field, the
use of statistics and acknowledging limitations can strengthen an author’s credibility, making
them more reliable sources. The use of decorum and sympathy is also very persuasive because it
appeals to the audience’s values and emotions. Ultimately, in healthcare, persuasion and rhetoric
are incredibly important because it can lead to a better understanding and wellbeing.
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References
Koukourikos, K., Georgopoulou, A., Kourkouta, L., & Tsaloglidou, A. (2019). Benefits of
animal assisted therapy in mental health. International Journal of Caring Sciences, 12(3),
1898–1905.
http://www.internationaljournalofcaringsciences.org/docs/64_koukorikos_review_12_3.p
df
MacWilliams, B., Schmidt, B., & Bleich, M. (2013). Men in nursing. The American Journal of
Nursing, 113(1), 38-46. https://doi.org/10.1097/01.NAJ.0000425746.83731.16
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Rhetorical Strategies in Machine Learning
Student Name
Dept. of Rhetoric & Writing Studies, San Diego State University
RWS 305W: Writing in Various Settings
Professor Centanni
10 October 2019
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Rhetorical Strategies in Machine Learning
Being part of a specific community entails sharing the same values as other members in
the community, as well as oftentimes following the same ways of communicating those values.
This is notably the case within the field of machine learning, which is a part of the larger field of
computer science and software. Machine learning, which happens to be a rapidly growing yet
somewhat controversial part of software, is essentially the use of algorithms and patterns to train
a machine to do or recognize certain things without explicit instructions. In this field, which
encompasses many new technologies like facial recognition and object detection, the typical
communication patterns become clear after reading through various scholarly journals on the
topic. Within the field of machine learning, authors employ statistics, popular references,
acronyms, and other strategies in order to appeal to their intended audiences and show their
authority within a discourse community.
Before examining how rhetorical strategies were used by the particular authors under
analysis, it is important to observe that common target audiences in this field includes students
and members of the field of computer science who may not know much about machine learning.
This becomes evident when each of the writings makes an effort to initially define what machine
learning and artificial intelligence are, and what purposes they may help serve. For example, in
the article “Philosophy and Machine Learning,” Canadian philosopher Paul Thagard (1990)
describes the aim of artificial intelligence as “getting computers to perform tasks that require
intelligence when done by people” (p. 261). By defining the aim of artificial intelligence in
easily understandable terms, Thagard makes it clear that his writing is meant to reach readers
who may not currently know much about machine learning or about programming at all. If
readers were expected to have an abundance of experience, definitions would not be necessary.
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This is only one sign of the audience, but being able to identify the intended readers clears up
why the authors choose to use the various rhetorical strategies they later employ in the interest of
persuasion.
One such strategy is citing statistics. Researchers Barrington et al. (2012), in “GamePowered Machine Learning,” cite statistics early and often in their discussion on machine
learning. By the second paragraph, the authors’ use of statistics shows the reader the power of
machine learning when used to create song recommendations for music listeners on different
streaming platforms. This is shown when Barrington et al. (2012) notes “after 10 [years] of effort
by up to 50 full time musicologists, less than 1 million songs have been manually annotated,
representing less than 5% of the current iTunes catalog” (p. 1611). Here, Barrington et al. use
statistics to convince the reader that humans who manually annotate songs lack the efficiency of
computer systems that do the same. By comparing human and machine data entry through
illustrative statistics, Barrington et al. are able to appeal to logos to show the immense difference
in efficiency between human annotating and the use of machine learning to accomplish the same
task. This is an important signifier of a field norm because readers in this field do not just expect
conjecture; they want to know that concrete data supports assertions. While statistics can
certainly be manipulated if used incorrectly, this field values those that are put into an
appropriate context.
While statistics are often utilized in illustrating widespread effects of machine learning to
audiences, it is also important that the authors are able to connect the subject to readers on a
personal level as well. A prime example of this is shown in the article “Machine Learning” by
Dellot and Balaram (2018). Early on in the piece, the authors show their effort to connect to the
reader by naming popular shows in which dystopian futures become the norm as a somewhat
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direct result of machine learning. They connect with readers by noting “popular culture is again
dominated by tales of machines gone rogue, from Ex Machina to Black Mirror” (Dellot &
Balaram, 2018, p. 44). Here, it is clear that the authors are trying to reel in the interest of the
audience by almost immediately connecting the subject to television shows they might be
familiar with or may have heard of. By doing this, they appeal to pathos in order to keep the
reader intrigued through imagery and emotional connection. It creates an immediate sense of
relevance in a reader, which shows that the argument is not just about dry claims and statistics,
but also about relatable concepts. Appealing to pathos through nostalgic experience or real-life
examples is an evident and seemingly necessary strategy frequently used within the field of
machine learning to create a connection with the reader.
Though creating a personal connection to the audience is important to stimulate the
interest of the readers in the widespread and statistic-filled subject of machine learning, it is
imperative that the reader is able to see the author as an authority within the discourse
community of computer science as a whole. One of the most commonplace ways authors are able
to show their authority in computer programming and belonging to the discourse community of
machine learning is through the use of acronyms. A simple example of this is shown when
Thagard (1990) almost immediately shortens artificial intelligence to AI (p. 261), but a more
complex illustration occurs when Barrington et al. (2012) shortens "Gaussian mixture model"
and "dynamic texture mixture" to GMM and DTM, respectively (p. 1614). Here, the authors of
both texts employ acronyms to abbreviate terms that they will use regularly in efforts to
demonstrate their understanding and experience with the subject of machine learning. To be sure,
knowing that AI means "artificial intelligence" is not insider knowledge; however, by meeting
the norm of employing the common acronyms on a frequent basis, these authors implant the idea
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that they do have further insider knowledge, which makes them authorities in the field. There are
many terms within the computer science and machine learning field that are long and specific,
and these acronyms both aid in creating a sense of belonging within the field and in allowing
people to reference concepts without adding confusing, lengthy definitions. By abbreviating
these terms, the authors are able to appeal to ethos in the subject of machine learning while also
allowing themselves to use the terms continually throughout the writings without tiring them out.
Although writers in the discourse community of machine learning do well in displaying
their experience within the field, one strategy that is notably absent from the field is authors
directly referring to the experience. In other words, they commonly show their experience
without talking about it. Whereas this may be a more suitable strategy to employ in other fields
of study, it is not often used when discussing machine learning, likely since the field is such a
new one that is rapidly growing and changing every day. Claiming 10 years of experience in a
field is not always particularly impressive, but few people have been able to study machine
learning much longer, since the earliest texts come from about 30 years ago, when the field itself
was still budding. With a field that is ever-changing and continually improving, it becomes
difficult to truly be an expert or to bring up experience with machine learning as past experiences
can quickly grow outdated as new technology develops. With these thoughts in mind, it is clear
why appeals to ethos are less often used in writings on machine learning.
The rhetorical strategies within the field of machine learning begin to reveal themselves
after reading through scholarly journals on the topic. Strategies used to appeal to pathos, logos,
and ethos assume a pattern and are utilized by members of the discourse community to show
their belonging to the field. Writers are oftentimes found using statistics in order to appeal to
logos, as well as using media and imagery to emotionally connect the reader to machine learning
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by appealing to pathos. Using acronyms to appeal to ethos become more apparent since this
rhetorical appeal is found few and far between within this discourse community. As the field of
machine learning continues to advance and grow, the rhetorical strategies used by members of its
discourse community are one element that seem to maintain a steady pattern.
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References
Barrington, L., Turnbull, D., & Lanckriet, G. (2012). Game-powered machine learning.
Proceedings of the National Academy of Sciences of the United States of America,
109(17), pp. 6411–6416. https://doi.org/10.1073/pnas.1014748109
Dellot, B., & Balaram. B. (2018). Machine learning. RSA Journal, 164(3[5575]), pp. 44-47.
https://doi.org/10.2307/26798354
Thagard, P. (1990). Philosophy and machine learning. Canadian Journal of Philosophy, 20(2),
pp. 261–276. https://doi.org/10.1080/00455091.1990.10717218
Assignment #1 Instructions (due at end of Week 5) **
*NOTE: Sample assignments can be found in the Week 3 Module!
Assignment #1: Rhetorical Analysis of Academic (or Professional) Texts in Your Field. Due by the end of Week 5. Reflecting on "Discourse Communities
& Communities of Practice" by Ann Johns and the lecture(s) from Professor Centanni, write a 4 to 6 page paper (not including title and reference pages)
that analyzes the rhetorical and linguistic norms of your field based on two (or more) typical texts from your discipline. NOTE: You are not merely
analyzing these two (or more) texts! You are analyzing these texts AS REPRESENTATIVE of your field. In other words, while you definitely
want to comment on what these authors do, make sure you keep your vision and analysis about how this represents the field as a whole.
It is important to note that a rhetorical analysis should not take a stance on the topic(s) of your text(s), nor should it make value judgments about if the
rhetorical norms in your field are "good" or "bad." In fact, try to eliminate all "praise" or "condemnation" language from academic writing. Instead, just
observe and examine the choices that the writers in your field make to appeal to their audience.
Also be sure to do the following:
1) Identify the target audience of each piece. Do not fall into the tempting trap of oversimplifying your readers as the "general public" or "common
people interested in the subject." Rather, look at specific elements within the text that show what assumptions the writer(s) hold(s) about their readers.
Focus less on concrete signifiers (i.e., don't worry about stating exactly how old you think a reader is or what level of education they have) and, instead,
try to identify the values this group of readers seems to share - and how you can tell. (This paragraph requires cited evidence.)
2) Identify 3 to 4 strategies the writers use. Each of these strategies should be analyzed according to their appeals (ethos, pathos, and logos);
however, APPEALS SHOULD NOT BE MISTAKEN FOR STRATEGIES. In other words, if the writer tells a sad story, you wouldn't say, "The author uses
pathos." You would say, "The author tells a sad story to appeal to the readers' pathos." So, one more time, DO NOT USE APPEALS AS YOUR
STRATEGIES.
After you identify the strategy, be sure to find a quote/paraphrase that illustrates the writer doing so, and then analyze how and why it would likely
persuade someone in the field. (These paragraphs require cited evidence.)
Your grade will be earned based on the following characteristics:
a) Genre expectations. This is a formal, undergraduate, rhetoric essay intended for an audience of academic readers who are not members of your
discipline.
DO: Use the academic voice 2. Write focused, effective introduction and conclusion paragraphs 2 that meet the expectations of this genre. Have a
strong thesis statement that organizes your main purpose for your reader. Structure body paragraphs 2 according to academic writing norms, with
specific topic sentences, contextualized evidence, and relevant analysis.
DON'T: Use casual language; mistake speech for writing norms (such as the dreaded One Word Opener or Second-person Question); skimp on analysis
(if you end a paragraph with a quote, you are not doing what is expected).
b) Formatting requirements. This paper must be formatted according to APA Style 7th Edition. You can have all of your questions answered by visiting
this Introduction to APA resource, and if you have questions about your citations or references page, see the APA: Citing Within Your Paper or
Formatting Your References Liste pages. Know that you are in complete control of these points, so be extremely mindful of your revision.
DO: Have a title page with your paper title, name, institution, course name and number, instructor name, and due date - formatted correctly. Have a
references page with the word References bold and centered at the top, all of your resources alphabetized, double-spaced, and hanging indented, and
meeting all other expectations. Do have one-inch margins, correct page numbers, and correct in-text citations for quotes and paraphrased material.
DON'T: Use MLA formatting, APA 6th, or anything else that is not APA 7th Edition; forget citations for material you quote/paraphrase that is not
original; leave out any of the above elements and expect to get an A!
c) Minimum assignment requirements. Four to six pages does not mean 3.5 pages. It also does not mean 7 pages. Part of good writing is editing to get
it where it needs to be. A two page range is HUGE, so please find a way to get your work into that window. Additionally, there should be a title page
and a References list - these do not count toward the 4-6 page requirement. NOTE: A five-paragraph essay will earn a maximum grade of "C." It is the
bare minimum, and cannot go higher.
d) Grammar, usage, and mechanics. Revise your paper as you write, but also look over it before you submit. There have been excellent papers that have
lost entire letter grades due to typos and lack of care. There have also been poorly written papers that gained points due to high levels of revision.
Again, this is an area completely in your control. Go to the Writing Center; use Grammarly; ask a friend to read it. There are ways to this part near
perfect.
Please be in touch with any questions, and carefully watch the lecture videos that help with this assignment. It regularly frustrates students who do
not seek outside help to see how "harsh" the grades are. The grades are not harsh if you take all necessary, mindful steps to achieve them. I will say
this one last time: THE RESOURCES ARE ALL HERE FOR YOU (including the professor himself), SO DO NOT EXPECT AN 'A' OR EVEN A'B' IF YOU
DO NOT TAKE THE TIME TO TEND TO ALL ISSUES ABOVE.
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