The Pebble Mine, a proposed project in Bristol Bay Alaska, has been at the forefront of conservation discussions for the past decade. In 2014, the EPA under the Obama administration blocked the project via the Clean Water Act. Discussions about the planned copper mine resurfaced recently after the EPA under Trump’s administration reversed the decision process by speeding up the “environmental review process” (Blate). The findings of this process, published in August, found that the mine will not cause any serious damage, allowing the U.S. Army Corps of Engineers to give the mining company permits for the project. Instead, to the surprise of most people following the controversy, the Corps told the mining company that they needed to address the mine’s impacts on the rivers. This declaration followed closely behind public outcry by Donald Trump Jr. and Tucker Carlson (of Fox News) who have both fished in the Bristol Bay (Blate). 

The power of Donald Trump Jr. and Tucker Carlson is particularly problematic when shown next to the decades long attempt by local native populations to stop the digging of the open-pit mine. The Yup’ik, Dena’ina, and Alutiiq people have relied upon the thriving ecosystems of Bristol Bay for thousands of years (Blate) through the healthy salmon fishery and the “intimate connection between the Tribes and their land and water” (Hadley). Although the upset of local communities has been broadcast since the original proposal of this project, the Corps didn’t change their permitting decisions until two influential, wealthy, conservative men, who have visited the bay only for recreation, spoke out against its development. The outcome of the Pebble Mine story is a striking display of power relations in the United States, particularly between native residents and the government who has the final authority over the construction of the mine. It is imperative that these relations are untangled and brought to light as they demonstrate a need to shift some decision making power away from the people and institutions who traditionally hold it. This is particularly relevant to the environmental justice movement, as marginalized communities are often paying the costs of development without reaping any of the benefits. 

How is it that it took a tweet from the president’s son to stop the development of the Pebble Mine when native communities have been speaking out against it since its proposal? 

Michael Foucault’s “The Subject and Power” offers an answer to this question by addressing power and how it works to subjugate people. When analyzing power relations, he argues that five topics must be addressed: “the system of differentiations… the types of objectives… the means of bringing power relations into being…forms of institutionalization… [and] the degrees of rationalization” (792). The system of differentiations refers to the differences between people who hold power and those who are being subjugated by power. Trump’s administration, the EPA, the U.S. Army Corps of Engineers, and the mining company hold the power to make decisions about the mine, while local residents, conservationists, and other interested parties don’t have the legal influence to determine the outcome. There are some striking differences between the members of these two groups; those in power (individuals and institutions) are wealthier and hold much more privileged than the people fighting to halt development. 

The types of objectives can refer to several things, one of which is “the accumulation of profits” (792). This mine is for copper, a resource for which many reserves have already been identified. In other words, this mine is not a necessary exploration, as the world has plenty of copper to meet its needs. Rather, the mine is a way for the mining company to make more money, despite its detrimental ecological and cultural effects. The means of bringing power relations into being is how the power is “exercised” (792). The power of the people making decisions about the mine is mainly institutionalized but is also enforced through economic and technological disparities. The government has historical power to make choices, and they also have a huge economic and technological advantage over the poorer and less connected indigenous communities.  Further, the government is connected institutionally to other powerful organizations like the U.S. Army Corps of Engineers and the mining company who also have lots of money and technological resources. 

Lastly, Foucault describes the degrees of rationalization, which addresses the “effectiveness of the instruments and the certainty of the results” as there are several actions that can be taken (792). The history of the Pebble Mine has complicated rationalization, because on top of the institutional power of the government (which employs effective instruments that led to very certain results), there is also the power of the voices of Donald Trump Jr. and Tucker Carlson, who had a significant influence over the Corps decision to suspend permitting. This power is less structural and harder to identify, which makes the degrees of rationalization less immediately visible. The hidden nature of this power makes it even more important to try and discern. All these influencers did was tweet and speak publicly about their experiences catching salmon out of Bristol Bay, which garnered enough attention and support to change the U.S. Army Corp’s decision. The two men did not employ extensive technology or use lots of money; they simply have enough sway over public opinion that they changed the results of a millions-of-dollars development project, while the voices of thousands of Native Americans were virtually ignored. 

The power relations displayed in this debate are one example of the problematic power distribution in the United States. While Fourcault’s writing helps demonstrate how the power of the government, the Corps, and the mining company functions, it is important to go a step further to challenge its pervasiveness. That the decision to suspend the project was made after the outcry of two men who vacationed at the bay, rather than the protests of people whose cultural traditions are reliant on its health is a blatant example of environmental injustice, as local communities livelihoods would be destroyed and they would never see the benefits of the project. In making development plans like Pebble Mine, discussions must be opened to include native people, local residents, and other invested parties who have been marginalized by uneven power distribution in the United States. 

References

Blate, Jessie Thomas. “Army Corps Puts the Brakes on Pebble Mine: Here’s What We Know.” American Rivers, American Rivers, 4 Sept. 2020.

Foucault, Michael. “The Subject and Power.” JSTOR, 1982.

Hadley, Kieran. “North America’s Biggest Mine Threatens Environment & Native Peoples.” The Yucatan Times, The Yucatan Times, 15 Aug. 2020.

Modern society has an inequality problem that spans across (arguably) every industry. Healthcare is towards the top of the list not only because it perfectly demonstrates the issue of inequality, but because it is an essential part of human health and well being. In the United States, it is no secret that racial minority groups receive lower quality healthcare than dominant groups. However, as new technologies develop through the analysis of big data, the healthcare field is making huge strides towards expanding public health. As the capabilities of the healthcare industry increase, we would hope that the benefits of these new technologies would be shared with everyone. But are these new, Big Data healthcare technologies really improving the health of all people?

One of the new technologies in healthcare that utilizes big data is called PRS, or Polygenic Risk Scores. These scores are used to predict the risks for people developing certain diseases and to suggest preventative measures accordingly. One study, published in Nature genetics, demonstrates that risk predictions from the PRS are far more accurate for people of European descent. The risk prediction of the PRS is most accurate when there is minimal genetic divergence between the person having their risk tested and the genetic data being used.

Figure 1: This figure demonstrates the differences in predictive power of the PRS technology between different racial groups. Each color represents a different racial group, labeled on the x-axis. The y-axis shows the predictive accuracy of the PRS for each group, or how well the technology can predict the risks of people of different races developing diseases. The plot is a violin plot, which means that is displays the distribution and the probability density (shape and area of each group) of the data. The varying width of each shape represents the frequency of the values of that predictive accuracy (the value on the y-axis) for each racial group. So, for the African population, the width of the shape towards the bottom of the y-axis indicates that the prediction accuracy for a majority of this population is between 0.15 and 0.2.

To address how predictive the PRS technology is for each racial group, the researchers used a well-powered genome-wide association study (GWAS). The genetic data for this study came from the UK BioBank. The usage of genetic data from the UK BioBank limits the generalizability of the results of this study and is not addressed by the authors. While the researchers conclude that the PRS is much more reliable for people of European descent, they neglect to specify their conclusions to the UK BioBank. Because the genetic data for the study came from a UK BioBank, it makes sense that the risk predictions would be more accurate for people with European ancestry. The authors should have specified their conclusions to be about the PRS results using genetic data from a particular biobank, or included data from other countries’ databases. If the study had included genetic databases from other countries, the PRS risk predictions might have been more accurate for people of other races and the conclusions of the study would have better addressed the global problem of inequality in genetic data.

That being said, there is a lack of genetic data from a lot of countries, which does limit people’s access to useful PRS scores. According to the study, 79% of genetic data comes from people of European descent, when they represent only 16% of the global population. Because of this overrepresentation of European descendants in genetic research, the PRS risk predictions for people who are not of European descent are not very accurate and therefore not very useful. Even if all countries with genetic data had been included in this study, there would have been missing data for a lot of racial groups (see Wikipedia’s  list of countries with a genetic database).

These PRS tests cost only about fifty USD per person and can be very helpful in educating people about preventative healthcare measures. However, these technologies, if further developed in the same way, will only exacerbate the inequalities that already plague modern society. Further, they are useless to a majority of the global population if we don’t work to ensure that all racial and ethnic groups are well represented during the collection of genetic data used to map these types of predictions. If Big Data in the healthcare industry is to be used in a socially just way, it must be taken for all groups; this process would ensure that the healthcare benefits that accompany new technologies are felt by people of every race.

References:

“DNA Database.” Wikipedia, Wikimedia Foundation, 5 Apr. 2019, en.wikipedia.org/wiki/DNA_database.

Egede, Leonard E. “Race, Ethnicity, Culture, and Disparities in Health Care.” Journal of General Internal Medicine. Blackwell Science Inc, June 2006. Web. 29 Mar. 2019.

Martin, Alicia R., Masahir Kanai, Yoichiro Kamatani, Yukinori Okada, Benjamin M. Neale, and Mark J. Daly. “Clinical Use of Current Polygenic Risk Scores May Exacerbate Health Disparities.” Sci-Hub. Nature Genetics, Apr. 2019. Web. 29 Mar. 2019.

“Violin Plot.” Violin Plot – Learn about This Chart and Tools to Create It, datavizcatalogue.com/methods/violin_plot.html.

Our society has an inequality problem that spans across (arguably) every industry. Healthcare is towards the top of the list of industries affected by inequality not only because it perfectly demonstrates the issue, but because it is an essential part of human health and well being. In the United States, it is well accepted that racial minority groups receive lower quality healthcare than dominant groups. Not only do these groups feel discriminated against in healthcare settings, but they are also less likely to receive preventative care (Egede). However, as new technologies develop through the analysis of big data, the healthcare field is making huge strides towards expanding public health. As the capabilities of the healthcare industry increase, we would hope that the benefits of these new technologies would be shared with all people. But are these new technologies, used in healthcare and other sectors, really benefiting everyone equally?

One of the new technologies in healthcare that utilizes big data is called PRS, or Polygenic Risk Scores. These scores are used to predict the risks for people developing certain diseases and to suggest preventative measures accordingly. One study, published in Nature genetics, demonstrates that risk predictions from the PRS are far more accurate for people of European descent. The risk prediction of the PRS is most accurate when there is minimal genetic divergence between the person having their risk tested and the genetic data being used. According to the study, 79% of genetic data comes from people of European descent, when they represent only 16% of the global population. Because of this overrepresentation of European descendants in genetic research, the PRS risk predictions for people who are not of European descent are not very accurate and therefore not very useful.

Figure 1: This figure demonstrates the differences in predictive power of the PRS technology between different ancestry groups.

These PRS tests cost only about fifty USD per person and can be very helpful in educating people about preventative healthcare measures. However, they are useless to a majority of the global population if we don’t work to ensure that all racial and ethnic groups are well represented during the collection of genetic data used to map these types of predictions.

This study is demonstrative of an overarching problem that comes along with the use of big data. Although technology that utilizes Big Data has the potential to be beneficial to a global population, a lot of it, like the PRS, is only valuable to select groups. These technologies, if further developed in the same way, will only exacerbate the inequalities that already plague modern society, and healthcare is not the only field exploring the benefits of these new algorithms. Technology that utilizes big data is being developed for almost every industry. Healthcare is joined by marketing, science, law, retail,and many more. In each of these fields there is already inequality, and if new technology is continued to cater to one group of people, not only will they remain at the top of the social hierarchy, but the gaps of inequality will grow.

Like the inequality gaps between racial groups, the gaps between people with varying socioeconomic status can also be expanded through Big Data tech. One mechanism to explain this trend is that not all competitors have equal access to Big Data. The companies that are able to invest in developing Big Data technologies are those that are already at the top of their sector: Google, Amazon, American Express, and other massive corporations. These companies are already monopolizing their industries, and their access to Big Data analysis has the potential to cement their dominance even further. Companies can use this new tech to determine customer demand better than ever before, and their prediction abilities will only increase as new technology is developed. This trend allows them to outperform competitors who don’t have access to Big Data. Small and local business owners already struggle to compete, and through new tech they will be left behind while the bigger corporations continue to grow.

As demonstrated by examples in two diverse industries, healthcare and marketing, Big Data has the potential to further inequalities that are already a huge issue in society. If Big Data is to be used in a socially just way, we need to ensure that it is taken for all groups, and is accessible to more than just the top 1%. If we don’t promote equality when Big Data is on the table, privileged and majority groups will further dominate, leaving many classes of people behind.  

References:

Egede, Leonard E. “Race, Ethnicity, Culture, and Disparities in Health Care.” Journal of General Internal Medicine. Blackwell Science Inc, June 2006. Web. 29 Mar. 2019.

Martin, Alicia R., Masahir Kanai, Yoichiro Kamatani, Yukinori Okada, Benjamin M. Neale, and Mark J. Daly. “Clinical Use of Current Polygenic Risk Scores May Exacerbate Health Disparities.” Sci-Hub. Nature Genetics, Apr. 2019. Web. 29 Mar. 2019

Humans often classify people as having “big personalities” or “no personalities.” These classifications have connotations, and grouping people into one of these categories has big implications for how we view that person. Although each of us has an innate sense of what it means for someone to have a big or no personality, we don’t have a standardized definition for either of these descriptors. Are there specific characteristics that we associate with people who we think of as having big or no personalities?

A study done at Ouachita Baptist University looks at patterns in character traits that we associate with people having big or no personalities. The study had one hundred and four participants that were recruited from Amazon Mechanical Turk. There were sixty four females and forty males; seventy eight of the participants were caucasian, eight were African American, seven were Hispanic or Latino, five were Asian, and the rest were either multi- or non- identified. The study used multiple questions and data analysis methods to look for characteristics that align with big and no personalities. First, participants were asked to define what it means to have “big” and “no personalities.” Next, they were asked to name two fictional characters (from movies, TV shows, books, etc.), one that has a big personality and one that has no personality. Lastly, the participants used the Ten Item Personality Inventory to rate their own personality and the personalities of both fictional characters that they chose. This inventory breaks down personality into five major (predefined) domains (extraversion, agreeableness, conscientiousness, emotional stability, and openness to new experiences).  To analyze the data, researchers first looked at participants definitions of big and no personality. They used the CQR-M procedure to group the responses into categories. The domains they created for no personality included four categories: boringness, low emotional expressiveness, low uniqueness, and reservedness, and the categories for big personality included eight categories: sociability, energy, uniqueness, emotional expressiveness, confidence and assertiveness, fun and humor, interestingness and complexity, and agreeableness. The next stage of analysis found that there is significant difference in the ten item personality index between characters with big and no personalities. According to the conclusions of the study, extraversion, agreeableness,and openness were much higher in characters who participants characterized as having big personalities. The researchers use these findings to assert that there is a common group of traits that people use to make judgments about whether someone has a big or no personality.

An important issue with this study is the limited sample. All participants came from Amazon Mechanical Turk, which is not representative of the population in the United States. Joining Amazon Mechanical Turk is elective and is likely to attract a specific demographic. The first major issue with the methods of this study is with the first stage of categorizing using the CQR-M procedure. During this part of the study, raters grouped the responses of participants (in which they defined big and no personalities) into larger categories. This is problematic because the people creating the categories have their own ideas about what it means for someone to have a big or no personality, and they might misinterpret or oversimplify participants responses to fit into their own predetermined boxes. There is was wide variety of how participants defined what big and no personalities are, and some of the things that were grouped together are highly questionable. The created domain of energy included words like “animated” and “lively” while the created domain of emotional expressiveness included traits related to extraversion. Should animated have been included under emotional expressiveness instead of energy? The line between these two domains seems very subjective, and similar questionable groupings were established for each domain.

This table shows the frequency of each domain description for people defined as having a lot of personality. Some of the domain categories had very low frequencies but were still used as a defining characteristics of people with “big personalities.”

One thoughtful step taken by the researchers was controlling for the role that each fictional character played (whether they were a protagonist, antagonist, supporting character, or incidental character). They found only one significant association between the character being incidental and the character being rated as having no personality. In the discussion section the authors argue that, because most of the nominated characters were protagonists and antagonists, that “main characters are simply more salient than non-main characters, and thus any differences may not be a reflection on the actual degree of knowledge about the character” (18). This defense of one of the limitations of their study is weak. If characters are incidental, by definition they do not have a major presence in the plot, so of course they will not have very developed personalities. To correct this problem, the study should have removed incidental characters from their data.

Overall, it is problematic to use this study to conclude that there are definable definitions for people having no personality and a big personality. We need to be mindful of trying to use these descriptors as if they have a standardized definition, because this study, although it claims to, does not prove that one exists.

Referenced:

Fayard, J. V., Clay, J. Z., Valdez, F. R., & Howard, L. A. (2019). What Does it Mean to Have “No Personality” or “A Lot of Personality”? Natural Language Descriptions and Big Five Correlates. Journal of Research in Personality. doi:10.1016/j.jrp.2019.02.004, https://sci-hub.tw/10.1016/j.jrp.2019.02.004

Gosling, Samuel D, et al. “A Very Brief Measure of the Big-Five Personality Domains.” Science Direct, Elsevier, 2003, gosling.psy.utexas.edu/wp-content/uploads/2014/09/JRP-03-tipi.pdf.

Modern, developed society is moving into an era of preventative healthcare. Instead of focusing only on how to cure diseases that people already have, researchers are increasingly concerned with determining how to prevent people from ever developing them. Because of the importance of diet in determining how healthy people are, a lot of preventative healthcare measures concern what people eat, and nuts are one food item that many scientists argue have lasting health benefits. One study done on people with diabetes claims that eating nuts, particularly tree nuts, can greatly reduce the chances of people developing cardiovascular diseases. Could eating nuts really decrease the likelihood of diabetic people developing heart issues?

The study, published by the American Heart Association, was done on 16,217 men and women with diabetes. The goal of the study was to compare the participant’s nut consumptions to their risk of developing cardiovascular diseases. Nut consumption of each participant was tracked using a validated food frequency survey, in which people had to input information about their dietary habits. Participants in the study updated their answers to the survey every two to four years. After collecting data about people’s food consumption, researchers did a follow up during which they assessed the frequency of cardiovascular disease and death in the participants. During the follow-up, scientists found 3,336 instances of cardiovascular disease and 5,682 deaths. The participants were broken up into categories based on their nut consumption. The health data was then compared between two groups of the participants, those who ate at least five servings of nuts every week and those who ate less than one serving of nuts every month. The authors of the study claim that people who increased their nut intake after being diagnosed with diabetes had an eleven percent lower risk of developing cardiovascular disease, a fifteen percent lower risk of developing coronary heart disease, a twenty-five percent lower cardiovascular disease mortality, and a twenty-seven percent lower overall mortality.

There are a few issues with the study that immediately stand out. For one, the authors provide no information about the sampling methods that they used. Although the sample size is relatively large, there are no clues as to whether the sample is truly representative of the population, everyone with diabetes in the United States. It is also concerning that the participants only had to update their food intake surveys every two to four years. People’s eating habits can vary quite a bit over the span of two years, and just because they ate a lot of nuts (or didn’t) for a few months out of one year does not mean that they consistently ate them (or didn’t) over four. To make this study’s results more conclusive, scientists should have tracked nut consumption much more frequently.

The researchers statistically controlled for a few outside causes of cardiovascular disease including sex/cohort, body mass index at diabetes diagnosis, smoking status, diabetes duration, nut consumption before diabetes diagnosis, and diet quality. In controlling for these variables, scientists can conclude that the changes in risk of cardiovascular disease that they observed was not caused by any of the factors listed above. Controlling for these variables was an important step, and the researchers argue that by controlling for these factors, they prove that the changes in cardiovascular disease rates were directly associated with nut consumption. However, the study needed to control for many other variables to ensure that they results that they got really were related to nut consumption. They should have also controlled for level of exercise, family history of diabetes and diet, and alcohol usage, just to name a few. It is possible that nut consumption is also related to these variables, and that there is no real association between nut intake and cardiovascular diseases.

The study’s questionable experimental and statistical design should make readers hesitant of their concluding claim. Although increased nut intake may decrease the risk of people with diabetes developing cardiovascular disease or dying, more research needs to be done before anyone can claim that there is a strong association between eating nuts and having a healthier heart.

The role religion plays in parenting is a commonly debated issue. Some parents argue that religion can be used as a tool to teach children compassion and respect. Others argue that children raised in religious households will have narrowed worldviews and weakened abilities for critical thinking. Does religion play a role in shaping the development and behavior of children?

A study published in Religions, an online journal, attempts to address how growing up in a religious household affects children’s development. The authors of the study claim that growing up in a religious home has both beneficial and detrimental effects on children’s development. According to the study, religiosity in the home improves children’s psychological and social development, but can be harmful to their performance on standardized educational testing.

 To conduct the study, the psychologists took data from when children were in kindergarten (to check the status of the religiosity of their household) and again in third grade to examine how religion impacted their behavior and development. The study was longitudinal, which is good for a long-term experiment testing for a correlation between different variables. The sample used in the study came from the Early Childhood Longitudinal Study- Kindergarten Cohort. The original sample included 21,260 children, and the authors claim that their sample is nationally representative. However, to remove a confounding variable, the scientists only included children in the study whose parents were married at the initial stage of data collection, when the children were in kindergarten. Because of this control, the sample size was reduced to 10,720 kids. Although this sample is smaller than the initial twenty thousand, it is still very large, which is a sign of a well-designed study. The authors don’t provide enough information about the sampling techniques to determine whether it is representative, which introduces some uncertainty into their results.

This table summarizes the observed results of the study. It shows whether each reported behavior supported each of the three hypotheses.

Reducing the sample size to control for the marital status of parents was just one of the confounding social variables that the scientists accounted for. The study also controlled for gender and race of the child, number of siblings under eighteen years old, family structure, family socioeconomic status, region, locale, and parents’ school involvement. By controlling for these variables, the study attempted to ensure that the changes in behavior were due to changes in the independent variable (religiosity) and not due to differences in the social variables listed above. Controlling for these confounding variables is a good sign that the study’s conclusions about the correlations between religiosity and children’s behavior are accurate. However, the scientists could not account for every confounding variable that might affect children’s behavior. For example, they did not account for the educational status of parents, for whether the grandparents were involved in the children’s lives, or for hundreds of other factors that could play a role in shaping the behavior and development of children. The study’s inability to control for other variables that shape behavior and development raises questions about the validity of their correlational conclusions.

Referenced:

Bartkowski, John P., et al. “Mixed Blessing: The Beneficial and Detrimental Effects of Religion on Child Development among Third-Graders.” MDPI, Multidisciplinary Digital Publishing Institute, 9 Jan. 2019, http://www.mdpi.com/2077-1444/10/1/37/htm.

In the United States today, there is a powerful debate about the existence of climate change, specifically regarding human-driven global warming. Although most climate scientists claim that there is strong evidence to suggest that the current warming of Earth’s climate is driven by anthropogenic influences, many people in the U.S. still have doubts about whether human action is leading to increased temperatures. With such strong scientific evidence backing up this global phenomenon, what is causing people to question its existence?

One common answer to this question is that people are simply ignorant about the science proving global warming. One study, conducted at George Washington University Law School by Donald Braman, Dan M. Kahan, Ellen Peters, Maggie Wittlin, Paul Slovic, Lisa Larrimore Oullette, and Gregory N. Mandel, claims that the divisions in beliefs about the existence of global warming stem not from a lack of scientific knowledge, but from people’s desire to interpret the science they read in a way that aligns with the popular ideas in groups that they associate with. The scientists argue that with as scientific literacy increases, people are actually less likely to believe in the risks that accompany a warming climate. Their findings are shown in Figure I, below.

Figure I: The first graph demonstrates what the results of the question “how much risk do you believe climate change poses to human health, safety or prosperity?” would be if science literacy and perceived risk were correlated. The second graph demonstrates the actual relationship between science literacy and perceived risk according to the study, with z-scores of perceived risk on the x-axis.

The study had a large sample of 1,540 participants. The authors of the study claim that the study was representative, and the large sample size is one indicator of a representative sample. However, the individuals included in the sample were members of an online testing group called Knowledge Networks. This group has a selection pool of 50,000 adults who are recruited to participate in various research. Voluntary online surveys can be problematic because they introduce non-response bias into the scientific conclusions of studies. Because some people may not respond to surveys that they are asked to participate in, the sample may not truly be representative of the entire U.S. population. The people that are willing to respond to a survey about global warming may be more passionate about the existence (or lack thereof) of the climate crisis, and may skew the results of the study.

Participants of the study were first asked about how big of a risk global warming poses to human health, safety or prosperity (on a scale of 1-10). They were then tested for scientific literacy, which is another questionable aspect of this study. Participant’s scientific literacy was determined by their answers to eight true-or-false questions, including “lasers work by focusing sound waves” and “antibiotics kill viruses as well as bacteria.” Although these questions may do a satisfactory job at testing participant’s general knowledge of science, they are not an accurate predictor of people’s knowledge of the science of global warming. Because human-driven climate change is a hugely politicized and popular issue, the science about climate change is much more accessible and talked-about than science about lasers or antibiotics; published science about climate change is consistently at the top of the lists on popular news sources. Because the science is frequently discussed, people likely have a much higher exposure to academic science about global warming than other areas of science. People’s knowledge of random scientific facts about lasers and antibiotics is not an accurate predictor of their knowledge about global warming.

Overall, the scientists at George Washington University Law School use their findings to argue that more published studies proving the existence of climate change are not the answer to solving the climate crisis. They claim that, in order to convince the general public that action needs to be taken to prevent further warming, literature published about climate change needs to be less focused on precise scientific findings and more focused on communicating the science in a way that doesn’t threaten the values of diverse groups. Although it is never a bad idea to broaden the audience of climate change discussions, scientists must be careful to avoid framing their findings in a way that represents a skewed version of the truth simply to accommodate the biases of broad social groups. This is especially true if the findings of the this study (that scientific literacy and perceived risk of climate change are not positively correlated) are not accurate.

Referenced:Braman, Donald, et al. “The Polarizing Impact of Science Literacy and Numeracy on Perceived Climate Change Risks.” GW Law Faculty Publications & Other Works, GW Law, 2012, scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=1298&context=faculty_publications

People often argue that spending time in nature can have lasting health benefits. Because of this, many people alter their lifestyles and living situations to include more nature. But is there strong scientific evidence to support the claim that being outside can decrease the likelihood of people developing certain health problems? More specifically, could exposure to diverse outdoor plant life decrease the likelihood of children developing asthma?

Professor Roger Ulrich and his team of students at Chalmers University of Technology claim that they found strong evidence suggesting that children who grow up in neighborhoods with diverse plant life are less likely to develop asthma. Using a database called Integrated Data Infrastructure (IDI) which is essentially census data in New Zealand, the researchers were able to track where children lived from birth to age eighteen. They then used satellite images to determine the greenness of the children’s neighborhoods and health records to determine whether or not the children developed asthma.

According to the study, children who live in greener areas with diverse plant life are 6.0% less likely to develop asthma. To explain this finding, the scientists use the hygiene hypothesis. According to this explanation, children that are exposed to more microbes develop stronger immune systems and are less likely to develop conditions like asthma. Because different plants have different microbes, according to this hypothesis, children living in neighborhoods with diverse plant life are less likely to develop asthma.

However, the scientists also found that an increase in certain types of plants, even in neighborhoods with high plant diversity, is associated with an increased risk of developing asthma. For example, children who live in neighborhoods with Gorse were 3.2% more likely to develop asthma. This made me wonder, is there truly a causal relationship between diverse vegetation in a neighborhood and the likelihood of a child developing asthma? A quick google search revealed that gorse is a noxious weed that weed controllers work to remove. What types of neighborhoods provide places for gorse to grow? Because it is a noxious weed, this plant is likely only thriving in poorer neighborhoods that don’t hire landscapers and noxious weed sprayers.

There are many factors that accompany vegetation diversity that could explain the link that these researchers found between plant life and asthma development. The scientists methods did not account for the potential tie between wealth and plant diversity. It is likely that in wealthier neighborhoods, there is a more diverse display of (non-noxious) plants, while in poorer neighborhoods, there is less focus on the diversity of plant life. Essentially, there other threats that accompany poverty (and a lack of plant diversity) that could increase the chances of a child developing asthma. For example, poorer neighborhoods likely have more pollution, inadequate healthcare, and less space for children to safely play outside- on top of having lower levels of plant diversity. Could these factors be causing children in neighborhoods with high plant diversity to stay healthy, and not the plants themselves?

Referenced:

Douwes, Jeroen, and Geoffrey H. Donovan. “Children Living in Green Neighbourhoods Are Less Likely to Develop Asthma.” Medical Xpress – Medical Research Advances and Health News, Medical Xpress, 8 May 2018, medicalxpress.com/news/2018-05-children-green-neighbourhoods-asthma.html.

Donovan, Geoffrey H., et al. “Vegetation Diversity Protects against Childhood Asthma: Results from a Large New Zealand Birth Cohort.” Nature News, Nature Publishing Group, 7 May 2018, http://www.nature.com/articles/s41477-018-0151-8.

Sources:

“Gorse: Ulex Europaeus.” Washington State Noxious Weed Control Board, Washington State, http://www.nwcb.wa.gov/weeds/gorse.