Link Blog: June 1, 2019

The racist origins of one of RVers’ favorite words: full-time RVing is on the rise, and so is the use of racial slurs (like “gypsy”) and cultural appropriation. (Full disclosure: I wrote this post, and Gluten-Free RV is one of my side projects.)

Google uses Gmail to track a history of things you buy — and it’s hard to delete. You can see your purchases Google has tracked here.

4 reasons why forgiving U.S. student debt makes sense: 1) borrowers’ overall debt could be reduced by over a quarter, even beyond the student loans themselves. 2) borrowers less likely to default on debts (no shit). 3) borrowers are more likely to relocate and get better-paying jobs. 4) overall spending and consumption could increase.

The use of male mice in drug research skews research against women: animal models have long been debunked as lousy ways to test drug candidates, but here’s one more nail in the mouse-shaped coffin.

Links: Raspberry Pi web services, the trouble with being inspiring, and the history of some important medicines

One of these days, I’m going to get myself on a schedule, and publish my links on a single day of the week.

First steps to running a web service on a Raspberry Pi: If you are interested in hosting your own Mastodon or Pleroma instance (or some other kind of more-involved web service), but haven’t a clue where to begin, this might help.

I am not here to inspire you. Sophia talks about the very serious problem of abled people finding disabled people inspiring.

Sir David Jack: an extraordinary drug discoverer and developer. David Jack’s goal as a pharmaceutical developer was to find “better medicines for the treatment of poorly-treated common diseases.” On a personal note, without David Jack’s invention of albuterol (brand name Ventolin), I would not be alive today. I think many of us can say the same, because Jack’s discoveries also include (but are not limited to) glucocorticosteroids for asthma treatment, ranitidine (Zantac) for gastric acid diseases, and ondansetron (Zofran) for severe nausea often associated with chemotherapy treatment.

Links: sunscreen, sunlight, facial recognition technology, and the atom bomb

Is Sunscreen the New Margarine? “Vitamin D now looks like the tip of the solar iceberg. Sunlight triggers the release of a number of other important compounds in the body, not only nitric oxide but also serotonin and endorphins. It reduces the risk of prostate, breast, colorectal, and pancreatic cancers. It improves circadian rhythms. It reduces inflammation and dampens autoimmune responses. It improves virtually every mental condition you can think of. And it’s free.”

Two US electric utilities have promised to go 100% carbon-free—and admit it’s cheaper. As the costs of carbon-based fuels skyrocket, this is unsurprising, but interesting.

Facebook’s ’10 Year Challenge’ Is Just a Harmless Meme—Right? The author explores the potential impacts of assisting facial recognition software with our 10-year-old selfies. The best scenario could involve training software that will aid in finding missing kids. The creepier scenarios involve a repeat of the Cambridge Analytica scandal.

Read the Scientific American article the government deemed too dangerous to publish: “In April 1950, the US federal government raided the offices of Scientific American Magazine to destroy every printed issue. ‘Three thousand copies already run off were burned, type was melted down, and every galley proof and script impounded.’ Three years later, Fahrenheit 451 was published without knowledge of this incident.” The banned article was about the moral meaning of the hydrogen bomb and its foreign relations implications.

PHP, SQL, and security

I have been working my way though Dr. Chuck Severance’s Web Applications for Everybody, via Coursera. It’s a four-course specialization that uses PHP and SQL, and I’m enjoying it very much. SQL is fun and interesting (and this will help when I go back to the Python Data Specialization), and PHP is one of those things I recognized in the URLs of browsers, and random error messages I’ve seen over the years, but I had no idea what it was. Now I can make some short programs to do some simple database work focusing on CRUD: Create, read, update, and delete.

I am also all-but-finished with a Cybersecurity for Business specialization. These courses rely on peers to review certain assignments, and the courses don’t seem very active with students– I’ve had trouble getting responses to questions, and right now I’m just waiting on another fellow student to submit an assignment so I can complete my last peer review and finish the specialization. It was pretty interesting– especially as a small business owner without a lot of experience but having read a lot of scary stories about hacks and leaks.

I’m working on re-working some of my PHP and SQL projects so that they don’t violate Coursera’s honor code when I include them in my portfolio. 🙂

Data Analysis Tools, week 1: hypothesis testing and ANOVA

Welcome to the first week of the second course in Coursera’s Data Management and Visualization specialization. In order to utilize ANOVA and post hoc testing, I needed to examine a different explanatory variable than in the previous course. This analysis will examine whether or not a person’s gender and race is associated with their support of the death penalty, as punishment for murder. I will still be using the Outlook on Life (OOL) surveys, made available by ICPSR for Coursera students.

Hypotheses to be tested:

Null hypothesis: the death penalty is supported equally among all gender-racial groups.
Alternate hypothesis: support for the death penalty varies among gender-racial groups.

The categorical response variable was a combination of two groups: those who know someone who has been arrested for a crime, and those who have a friend or relative who has been convicted of a crime. This variable had two categories: yes and no.

The new categorical explanatory variable contains four (4) categories: white men, white women, men of color, and women of color. The gender choices were extremely limited in the dataset: male, female, and no response. Because it was not possible to know if a “no response” was a refusal to answer or identifying as a transgender or nonbinary person, these individuals were omitted from the sample. For more comprehensive data analysis, the survey should have used “masculine” and “feminine” instead of “male” and “female,” and included options for transgender, nonbinary, and possibly other gender identities.

An analysis of variance (ANOVA) revealed that among this sample, the gender and race of an individual (collapsed into 4 categories, as the categorical explanatory variable) is significantly associated with a preference for the death penalty. Utilizing an ordinary least squares (OLS) approach, the following results were obtained: F-statistic = 32.57, p = 2.10e-20. Tukey’s Honestly Significant Difference post hoc test was conducted to determine which groups were significantly different from each other. There was no significant difference in the results between white men and white women, therefore we accept the null hypothesis; however, there were significant differences between white women and men of color, white men and men of color, men of color and women of color, white women and women of color, and white men and women of color, and we accept the alternate hypothesis for these groups.

The punishment preferences among the groups are as follows: 66.7% of white men favor the death penalty, 58.8% of white women favor the death penalty, 55.6% of men of color favor imprisonment, and 64.7% of women of color favor imprisonment.

I was unable to calculate standard deviation for these results. I do not think this is possible, because the explanatory variable has 4 categories, and the response variable has 2 categories: neither are quantitative. After spending many hours (at least 16!) trying to find and code quantitative variables relevant to my original thesis, I was unsuccessful. I understand the code involved in calculating means and standard deviations, but I was unable to show that in this assignment. If any of my classmates have some input or resources, I’d welcome the assistance. I would very much like to calculate the deviation between each of the four gender-ethnic groups.

Click to read the rest….Data Analysis Tools, week 1: hypothesis testing and ANOVA

week 1: getting started

Saluton, miaj samklasanoj! [hello, my classmates!]

I am going to examine whether people’s opinions of prisoners might be more favorable if they themselves have been accused or convicted of a crime or if they know someone who has. I believe that people who have firsthand experience with the defense side of the criminal justice system are going to be more empathetic towards people who are in jail or have been accused of a crime. I also believe this group will be more likely to oppose the death penalty or “three-strikes” laws (life in prison after a third felony conviction).

[Note: links to sources are embedded in the text below.]

I will be utilizing the Outlook on Life (OOL) surveys, made available by the Inter-university Consortium for Political and Social Research (ICPSR) and a special and generous arrangement that allows Coursera students to access this data. I get to take advantage of accessing a dataset that I would not have access to otherwise. This data was collected between August and December 2012. The target population was adults over the age of 18, divided into four groups of male- or female-identified African-American or Black individuals, and white or other (non-Black) race individuals.

A review of the prisoner’s rights movement between 1960 and 1980 suggests prisoners have the power to change public opinion and policy through the actions they’ve taken to advocate for their rights while incarcerated.

An interesting paper from 2008 describes public preference for “punitive policies,” but in the case of non-violent offenders and extreme punishments like the death penalty, support is weaker, and may indicate a preference of rehabilitation over the most severe of punishments.

A study in Chicago in 1988 found that despite increased punitive sentences, the general public was more supportive of the idea of rehabilitation over incarceration. However, a 2008 study in the United Kingdom found that people are perceiving crime to be more prevalent and violent despite falling crime rates, and that people feel less safe, implying that they may be more likely to prefer punishment over rehabilitation.

A 2003 article compared recidivism rates of sex offenders with public opinion and concluded that punishments are disproportionately severe and based on prejudices regarding recidivism rates, despite statistics on treatment options and recidivism rates.

In Perth, Australia, a survey of 544 residents in the 1980s found that people were more likely to recommend the minimum of three potential sentences when they were given the description of specific cases, compared to when they were asked for potential sentences for a general offense, suggesting that people may be more empathetic towards the accused if they see them as individual people rather than anonymous criminal offenses.