Understanding Family Communication Patterns: Pluralistic vs. Protective

1 Course Paper 2 Zachary Hansen Department of Communication, University of Washington COM 200: Introduction to Communication Anis Rahman February 15 th , 2024

2 Prompt 1 Two types of family communication patterns evident in many families are the pluralistic and protective patterns. Rahman (2024a) defines four key patterns: pluralistic which involves high conversation and low conformity; protective characterized by low conversation and high conformity; laissez-faire with minimal conversation and conformity; and consensual reflecting some conversation and conformity. In a pluralistic family communication pattern, there is much open and frequent dialogue between parents and children with few rules restricting the child (Rahman, 2024a). An example may be Jane's family where she is greatly encouraged to share her deepest thoughts, dreams, aspirations, and struggles with her parents regularly. Her parents consciously try to have in-depth conversations about her interests in social justice, letting Jane take the lead. There are few firm rules set in stone – her curfew is flexible as long as she checks in, birth control is openly discussed, and her friends are welcomed into their home. This pluralistic style gives Jane more independence and room to voice her authentic thoughts and beliefs. In contrast, a protective family communication pattern strictly limits open dialogue between parents and children (Rahman, 2024a). Expectations for obedience tend to be very high and parental oversight is pervasive. We can envision this protected family dynamic in Eric’s household. His parents rarely inquire about or show interest in the details of his social life, dating relationships, or school pressures. Instead, rigid rules dictate Eric’s behavior – he has an early and strict curfew, driving restrictions, and mandatory family meals. His parents convey their expectations almost exclusively through limits rather than two-way conversation. This closed style shelters Eric yet also stifles his independence and development.

3 In summary, while pluralistic and protective communication differ markedly in openness of dialogue and limits on children, they may aim for similar goals of appropriate development and guidance. A pluralistic style emphasizes rich conversation to guide while a protective style prioritizes rules over interactive dialogue. Neither style is inherently "better" than the other. The success of each depends on fit with the family's contextual needs and preferences. However, Rahman (2024a) suggests that higher levels of open communication may better enable children to develop strong coping skills for life's inevitable challenges. Finding the right balance is key.

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4 Prompt 2 Phil Davison violated several nonverbal display norms during his speech that likely contributed to him not getting the Republican nomination. First, his use of proxemics or personal space was excessive and intimidating. He paced rapidly around the stage and at one point walked 20 feet away while staring intensely at the audience. This aggressive invasion of others' space conveyed a threatening rather than passionate tone. As Rahman discussed, political speeches require disciplined use of space to engage the audience without intimidating them. Davison failed to adhere to those proxemic norms. Second, his eye contact or gaze was aggressive, rather than engaging the audience. His direct stare while pacing far across the stage came off as menacing. As noted in the materials, eye contact helps focus attention and should regulate interaction. However, Davison’s sustained staring while encroaching into audience space had a domineering effect that likely made viewers uncomfortable. Finally, his facial expressions were extreme and appeared inauthentic, like he was forcing emotions rather than genuinely expressing passion for his message. For example, his furrowed brows, flaring nostrils, and exaggerated frowns seemed overdone given the context. As the content indicated, appropriate facial signals match the tone of verbal messages. Here, Davison’s dramatized grimaces contradicted rather than reinforced his statements about qualifications and public service. While Davison may have intended for his animated nonverbal cues to demonstrate his dedication, they likely had the opposite effect. As Rahman (2024b) discussed, when nonverbal displays strongly contradict the verbal content rather than accentuate or regulate it, the message gets lost. Here, Davison’s words highlighted his credentials and interest in public service, but his intimidating nonverbals distracted him from that substance. His aggressive spatial invasions,

5 stare-downs, and dramatic facial grimaces diminished viewer focus on his statements about experience and qualifications. Ultimately this nonverbal-verbal contradiction likely contributed to his failure to secure the nomination. Moreover, the excessive nonverbal displays probably amplified perceptions of Davison as unstable or even threatening. As Rahman (2024b) noted, multichannel nonverbal cues are complex to decipher. Viewers tend to approximate meaning based on norms for context. Here, most expected a professional tone befitting a political speech. Davison’s perceived volatility violated those expectations. Consequently, while he may have wished to demonstrate passion, his provocative spatial proximity, eye contact, and facial displays may have instead left viewers feeling he seemed irrational or dangerous. Had Davison calibrated his nonverbals to align better with rather than overwhelm his central message, he may have come across as more reasonable and compelling.

6 Prompt 3 I selected YouTube as the platform to examine for this experiment, as it is the site I use most regularly and have the greatest familiarity with when it comes to its recommendation algorithm. My YouTube watching largely serves as entertainment, though I also often gain new knowledge from more educational channels like Exurb1a, Vsause, and Kurzgesagt. Upon launching YouTube at the start of this experience, the very first thing I observed was homophily. Homophily refers to the practice of customized content recommendations based on a user's past interactions. By tailoring suggestions to align with what the user has previously seen or clicked on, the goal is to provide information supporting their viewpoint. The very first thing YouTube shows me is related videos/content from the last time I used YouTube (see Appendix 1). As I continued through YouTube's algorithm, the recommended videos all aligned strongly with my interests. I would click through to watch many of them since they were relevant topics for me. These personalized suggestions are meant to engage each unique user - every person likely receives recommendations catered to their individual preferences and past viewing behavior. By expertly customizing suggestions to match what people have previously searched or engaged with, YouTube boosts user interaction across its massive platform. Even the advertisements that play amid my video watching are selected to relate to other content I’ve interacted with. During my experiment, YouTube’s recommendations have steered me toward certain cultural spheres and communities online— this is a phenomenon known as social sorting. This refers to when platforms connect those who share common interests or characteristics. On one hand, seeing content and discussions tailored to my existing likes can help me bond over shared passions and beliefs. However, it also risks limiting my exposure to alternate viewpoints.

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7 YouTube’s amplification of preferred topics shapes my online circles. While social sorting can positively reinforce identities, the tradeoff can be intellectual isolation in echo chambers. This experiment highlights several concerning issues stemming from YouTube's recommendation algorithm. While tailored recommendations perfectly suited to already-held beliefs can be enticing to the individual, downsides arise when those beliefs are prejudiced or misinformed. This can happen because algorithms reflect the priorities and perspectives of the human designers who build them. If those designers have real-world biases, they can unintentionally "encode" those same biases into the systems they create. Stereotypes, generalizations, and false beliefs that circulate in culture further spread through these algorithms, contributing to narrower worldviews and polarized beliefs. As companies like YouTube become so adept at hyper-personalization, always matching individuals with more of what they like, they run the risk of deepening social divisions. Their content assessments occur within biased systems, limiting exposure to opposing views and minorities. Furthermore, pursuing "perfect recommendations" above all else prevents progress toward improving critical thinking, empathy, and inclusiveness.

8 Prompt 4 During the lecture on surveillance and privacy, Professor Rahman argued that certain digital apps and platforms function as "panopticon watchtowers" that facilitate surveillance and control on behalf of tech companies and the military-state industrial complex. He utilized the lens of panopticonism theory, which views society as a digital prison where citizens internalize a sense of constant observation, and presented the case of Google's involvement in the NSA's PRISM mass surveillance program as evidence for this argument. Specifically, Professor Rahman (2024c) expanded on how the network effect incentivizes technology companies to amass vast amounts of user data. As more consumers use Google services, more data gets extracted from their searches, emails, locations, and app usage. This data boosts Google's market domination and profits by enhancing ad targeting, product improvements, and population monitoring capacities. Rahman (2024c) stated that "more surveillance leads to more market value, more profit, but it means also more surveillance”. This initiates a self-perpetuating cycle whereby technology platforms’ accumulation of user data increases profits which drives further extraction of data to further maximize financial gains. As an example, Google bought YouTube in 2006 for $1.65 billion to access YouTube's large user base and data to show targeted ads, therefore increasing surveillance and profits. Furthermore, Professor Rahman (2024c) revealed that Google has close ties with the U.S. military and intelligence agencies like the NSA. He presented the example of when Google purchased Boston Dynamics, an advanced robotics company that works closely with the Department of Defense. While Google later sold Boston Dynamics, Professor Rahman argued they were mainly interested in acquiring robotic patents and expertise to strengthen partnerships with the military.

9 Additionally, Professor Rahman (2024c) discussed how the NSA's PRISM program recruited Google, Facebook, Apple, and other large tech companies to secretly collect private user data in the name of national security. He cited the revelations of whistleblower Edward Snowden, which exposed how these platforms provide backdoor access to the NSA without users' knowledge or meaningful consent. This demonstrated the deep entanglement between the tech industry and military-state surveillance tools. Overall, I agree with Professor Rahman's argument that certain major digital platforms serve as "panopticon watchtowers" to enable corporate and government surveillance through centralized data gathering aligned with military-technology interests. The evidence on Google's network effect motives with YouTube, its robotics connections with Boston Dynamics, and secret PRISM program involvement supports this view. While users may consent to corporate data sharing, the Snowden leaks revealed how our data can be appropriated for hidden agendas like unauthorized state surveillance without transparency or true consent. I agree that this poses threats to privacy and democracy in the digital domain. More oversight and accountability of big tech companies’ drive for data, profits, and state alignments with the military arena are warranted.

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10 References Rahman, A. (2024a). Lesson 5: Family Com & Nonverbal Com [Video lecture]. University of Washington, Introduction to Communication Course Canvas: https://canvas.uw.edu/ Rahman, A. (2024b). Lesson 5: Family Com & Nonverbal Com [Video lecture]. University of Washington, Introduction to Communication Course Canvas: https://canvas.uw.edu/ Rahman, A. (2024c). Lesson 6: Surveillance and Privacy [Video lecture]. University of Washington, Introduction to Communication Course Canvas: https://canvas.uw.edu/

11 Appendix Screenshot 1: https://drive.google.com/file/d/1Eed6Nw7DFhymy60uWH_ytswtdk_RVvGH/view?usp=sharing Screenshot 2: https://drive.google.com/file/d/1npNWPGG9XzbA-kUIBrvffYpze5aELN5h/view?usp=sharing Screenshot 3: https://drive.google.com/file/d/1aq6LYW2pynpR_rgOUdB0LhPKRe8jch3z/view? usp=drive_link

12 Screenshot 4: https://drive.google.com/file/d/1eRPVo7yiXli7fze1WTTctI4rW-INYOnv/view?usp=drive_link Screenshot 5: https://drive.google.com/file/d/1iS6acytVSHvSqQXNpLsfW2jLoE6MaOvh/view? usp=drive_link

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13 Screenshot 6: https://drive.google.com/file/d/1nt5qBz19bbMQTQtt6vGOCgSOIPWCvX-M/view? usp=drive_link Screenshot 7: https://drive.google.com/file/d/1sZZgsGuiUGRZcvVLYoalzqz1O07KL-Tm/view? usp=drive_link

14 Screenshot 8: https://drive.google.com/file/d/1_llkSHIgeblQF5ErpOBhmLfrf0lZsn5k/view?usp=drive_link Screenshot 9: https://drive.google.com/file/d/1iCFatpqT9MrK2mkkxODyg_mj2Pi0ZCKP/view? usp=drive_link Screenshot 10: https://drive.google.com/file/d/1oTi3nC03FUMti1BC8-BzK8PG9VUln6I-/view?usp=drive_link

15 Screenshot 11: https://drive.google.com/file/d/1umXxXdVwY_1uPzwHfmyG8MVJQBPJdjMa/view? usp=drive_link Screenshot 12: https://drive.google.com/file/d/1JW5TWqJ-v0za-wQBpa_3inISb14N5W-A/view?usp=sharing

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