According to functionalist sociologist Murdock, what are the four essential functions that families perform in society?

Reproduction, economic support, socialization, and emotional support

Environmental sustainability, educational development, artistic expression, and community engagement

Political governance, religious adherence, cultural preservation, and entertainment

Military training, technological innovation, healthcare provision, and legal administration

According to sociologist Talcott Parsons, what are the two main functions of the family in society?

Primary socialization and stabilizing adult personalities

Economic production and political governance

Healthcare provision and educational development

Religious instruction and cultural transmission

Sociology of the family

Studying population involves examining various aspects of the demographic composition, dynamics, and characteristics of a group of individuals within a specific geographic area. Here are key areas to explore when studying population: Demography: Population Size: Analyze the total number of individuals in a given area or community. Population Distribution: Examine how the population is spread across different regions or areas. Population Density: Explore the concentration of people in a particular area relative to the size of that area. Population Growth and Decline: Birth Rate: Study the number of live births per 1,000 people in a given year. Death Rate: Examine the number of deaths per 1,000 people in a given year. Migration: Explore patterns of people moving into and out of a specific area. Age Structure: Age Distribution: Analyze the distribution of individuals across different age groups in a population. Dependency Ratio: Examine the ratio of dependent individuals (young and elderly) to the working-age population. Gender Composition: Sex Ratio: Study the ratio of males to females in a population. Population Pyramids: Use population pyramids to visualize the age and gender structure of a population. Fertility and Family Planning: Total Fertility Rate (TFR): Explore the average number of children a woman is expected to have during her reproductive years. Contraceptive Use: Analyze the prevalence of contraceptive methods in a population. Mortality and Health: Life Expectancy: Examine the average number of years a person can expect to live. Infant and Child Mortality: Study the number of deaths among infants and children under the age of five. Urbanization: Urban vs. Rural Population: Analyze the distribution of people in urban and rural areas. Rate of Urbanization: Explore the speed at which people are moving from rural to urban areas. Education: Literacy Rates: Examine the percentage of the population that can read and write. Educational Attainment: Study the level of education achieved by individuals within the population. Socioeconomic Factors: Income Distribution: Explore the distribution of income among the population. Employment Rates: Analyze the percentage of the working-age population that is employed. Cultural and Ethnic Composition: Ethnic Diversity: Examine the presence of various ethnic groups within the population. Healthcare and Quality of Life: Access to Healthcare: Study the availability and accessibility of healthcare services. Quality of Life Indicators: Explore factors such as housing, sanitation, and overall living conditions. Population Policies and Government Interventions: Study the impact of government policies on population dynamics, including family planning programs and immigration policies. Environmental Impact: Explore the relationship between population growth and its impact on the environment, including resource consumption and pollution. Global Population Trends: Examine global population trends and their implications, including aging populations and population growth in developing countries. Studying population involves a multidisciplinary approach that incorporates elements from demography, sociology, geography, economics, and public health. Researchers and policymakers use this information to make informed decisions about resource allocation, development planning, and social policies.

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

The social perspective & Basics of Sociology

The concept of social stratification in Sociology

The topic of Life chances, Social problems and inter and intra group conflict topics in sociology

Three ways to do sociology * objectivity, replication and value free research) Also the use of dependant and independat variables

Chapter 7 - Review Data and Decision Making *Glow bus due at midnight, name and student number: answer questions using content in class People have created wonderful things for centuries, and management Management can be traced as far back as 500 bc when the ancient Sumerians used written records to improve government and business activities Why is it important to lean from the past Not to repeat our mistakes Classical management approaches Scientific management Administrative Principles Bureaucratic organisation Behavioural Management Approaches Follett’s Organizations as communities The Hawthorne studies Maslow’s theory of human needs Mcgregor’s Theory x and Theory Y Argyris Personality and organisation Modern Management foundations Organises as systems Contingency thinking Quality management Quantitative and analysis and tools Evidence-based management Contributions Frederick Taylor - Father of Scientific management He noticed that workers often did their jobs with wasted motions and without a constant approach. His resulted in inefficiency and low performance He believed the problem could be fixed if workers were taught to do their jobs in the best ways and ten were helped and guided by supervisors Four guiding principles of scientific management Rules of motion, standardized work and proper working conditions Select workers with the right abilities Train workers and give them incentives Support workers by planning and smoothing the way as they do their work Frank and Lillian Gilbreth Pioneered use of motitono studies as a management tool In one famous case, the gilbreaths cut down the number of motions used by bricklayers adn tripled their productivity Contributions from scientific management Make results-based compensation a performance incentive Carefully design jobs with efficient work methods Carefully select workers with the ability to perform the job Trian workers to execute activities to the best of their abilities Train supervisors to support workers so they can perform jobs to the best of their abilities Classical Management Adiminstative principle (Henro Fayol) 1919, after a career in French industry, Henri F published “adminisration Industrielle et Generale” (General and industrial management) in which we out like his views on the management of organiztion and workers Rules and duties in management Foresight - to complete a plan of action for the future Organization - To provide and mobilize resources to implement the plan Common- to lead, select and evaluate workers to get the best work toward the plan Coordination- to fit diverse efforts together and ensure information is shared and problems solved Control- to make sure things happen according to plan and to take necessary corrective action Classical management Bureacratic organiztion (Max Weber) Max weber (Bureaucrativ organization) - late 19th century German political economist who had a major impact in the fields of management and sociology Bureaucratic Organization An ideal, intentionally rational adn very efficient form of organization Based on the principles of logic, order and legitimate authority Characteristics of BO Clear division of labour Clear hierarchy of authority Formal rules and procedure Impersonality Careers based on merit What are some disadvantages of bureaucracy Takes a long time for problems to become solved bec there are procedures and there is a chain of people in command Having the power Rules have to follow Excessive paperwork or “red tape” Slowness in handling problems Rigidity in the face of shifting needs Resistance to change Employee apathy Behavioural Management Approaches (focus on understanding the elements that affect human behaviour in organisations) Follett’s Organizations as communites Mary park follett contributed to the transition from classical thinking inot behavioural management Groups and human cooperation Groups allow individuales too combine their talents for a greater good Organizations are cooperating “communites” of managers adn workers Managers job is to help people copperate and achive an integration of goals and intrests Forward-looking managment insight: Making every emploee an owner creates a sense of collective responsibility Prescursor of employrr ownership, profit sharing and gain sharing Buniess problems invovle a varity of inter realted factors Prescursor of systems thinking Private profits realtive to public good Precursor of managerial ethics and social respinsibility Hawthorne studies Took place at western electric chicago plan, a tran led by Harvards Elton Mayo set out to learn how econmic incentives and workplace conditions affected workers output Maing objective Intial study examined how ecomoin incentives adn physical conditions affected worker output (productivity) No consistent relationship found During experientmetn they had 2 groups The expertiant groups (impoved wokring ocnditions ) The control group ( no changes to original working conidtions) No consitant relationship found, perfomance in both groups increased even after removing incentives Social setting and human relations Concluded New “social setting” led workers to do good job Good “Human relations” = higher productivity The contect - The Great Depression (1929-1940) Employee attitudes and groups processes Osme thinsf satisifed some workers but not others People resticited output to adhere to groups norms (Avoid layoffs) Lessons from he hawthrone stufirs Social and human concerns are keys to prductivity Hawthrone effect - People who are singled out for special attention perform as expected Maslow’s Theory of human needs Human needs The work of psychologist Abraham Maslow in the area if human “needs,” also has had a major impact in the behavioual apporach to management Maslow’s hierarchy of human needs Self actualization needs Higherst level: need foe self fulfillment to grow and use abilites to fullest and most creative extent Esteem needs Needs fro esteem in eyes of others need for respect, prestige, recognition; need for self esteem, personal sense of competence, mastery Social needs Need for love, affection, sense of belongingness in ones relationship either other people Safett needs Need for security, protection and stability in teh events of day to day life Physiological needs Most basic of all human needs: need for biological maintence; food, water and phydical well being Principles Defict principle: A satidifed need is not a motivator of behaviour Progress principles: A need becomes a motivator once the preceding lower-level need is satisfied Both principles cease to operate at self actulilzation level McGregor’s Theories Thepry x assumes that workers; Dislike work Lack ambition Are irresponsible Resist change Prefer to be led Theoyry y assumes that workers are Willing to work Willing to accept responsibility Capable of self control Capable of self direction Imaginative and creative According to McGregor, Managers create: Self fulfilling prophecies Implications of Theory x and y Theory x managers: Create situations where workers become dependent, passive and reluctant Theory y managers create situations where workers respond with initiative and high performance Central to notions of empowerment and self management Argyris’s theory of adult personality Classical management principles and practices inhibit worker maturation and are inconsistent with the mature adult personality Management practices should accommodate the mature personality: Increasing task responsibility Increasing task variety Using participative decision making Modern Management Foundation Quantitative analysis and Tools Analytics: the use of large data bases and mathematics to solve problems and make informed decision using systematic analysis Organization as systems System Collection of interrelated parts that function together to achieve a common purpose Subsystem A smaller component of a larger system Open systems Organisations that interact with their environment Contingency thinking Tires to maths managerial responses with problem (situation) No “one best way” to manage The “appropriate way to to manage depends on the situations Quality management Qality anc competitive advantafe are linked Total quality managment (TQM) Comprehensive approach to contiou impovment on teh entire organization ISO certification Gloval quality management standards Refine and upgrade quality to meet ISO requirments Evidednce Based Managment Making management decision on “hard facts” about what really works

Aqa a level sociology the social construction of childhood

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