Forgotten Mountains

Though the years separate us,

Walls divide us,
Pain and betrayal build our defenses,
There's a secret magic moonshine
From my dreams as a small boy
Lighting the way to Montana,
Where for once we were happy together,
The four of us,
When we were still a family
That loved each other.
Then we hit a frantic deer
And the nightmare began,
Casting all its stress unto us,
A life ended too short,
A family that couldn't breathe.
Hope turned to despair
As blood splattered the car,
As the summer faded away.
But every storm dissipates,
Shedding clouds from the tornadic yelling,
Offering forgiveness
Like the mountains that peaked
On glassy diadem east of Flathead,
Where joy reunited with sorrow.
That's where you'll find me,
A little boy waiting
For his family to return.

Your son dug for data
In the digital firmament
While you and your wife
Rattled suburbia in a coma,
Motorcrash from the moon circuit
Dazzled the darkness
In lingering lightning alleviated
By crowd cheer, wind shear
And the mayhem whirlwind
That sent our house flying
On wings of remorse spread
Through rain splash off the window pane
Where the one you abandoned slept
To keep safe from your wrath,
Oh, the unbridled wrath
Hollering through comfort to be heard
Across the arena deserted,
Destroyed by sonic ear crash
That came from east of hell
To impregnate your ignorance
With the fact you were his father.

Here the water is so blue, so clear
In the alpine basin of ancestral warmth
That first consecrated from glaciers
Reflect off the leaking skin
Like spires in a frozen mirage.
The cobalt skyline is a snowflake's edge,
Bordering the nexus where mountain
Spirits dwell in a tephra cradle,
Where the smell of loam and pine
Blends a potion of serenity so distinct
As to fertilize an aboriginal paradise.
We did not share their heritage
But were welcome anyway
For rejecting the urbanite traditions
That defiled the body and mind.
The sound of campfire sparks,
Flaring like mad behind the melody
Of mantras devoted to outcasts
Drew me in toward their faces,
Their vacant eyes staring into nothing.
The voices melted our grudges
Into puddles of remembrance
That merged into the fire
Disintegrating the flames,
Revealing starlight reborn.

Article Review: Do Jobs Still Attract People?

Introduction

            In the past, urban growth projections in the U.S. traditionally relied on the basis that people move to where jobs are available (Graves et al 2020, 323).  However, some recent trends have indicated that this relationship is no longer causing urban migration.  The researchers in this article attempted to determine whether it is still happening, and whether there is a variation between metropolitan size and economic base (ibid. 324).  They determined this by using statistical methods to evaluate population and employment data for 50 large metropolitan areas in the U.S. (ibid. 324).

 

Literature Review

            According to several studies, the expectation that population growth should be proportional to job growth has become outdated (ibid. 324-325).  Recent studies on the topic indicate that a behavioral shift has occurred, showing how more people are migrating to cities for a better quality of life and not necessarily better jobs (ibid. 325).  Most studies have shown that jobs follow people rather than the other way around (ibid. 325), however they did not involve spatial parameters that would explain regional variation.  A statistical analysis of the deviation between a metropolitan city’s jobs-population relationship can help identify spatial patterns in urban growth projections.

 

Background and Geographical Context

Most of the cities evaluated were in top 50 U.S. metropolitan areas by population, including New York, Boston, and Houston.  Data for the cities was retrieved from the U.S. Bureau of Economic Analysis (ibid. 326), a government organization that has only been keeping track since 1970.  The date range for this study started from the same year it was organized: 1970-2017 (ibid. 326).

 

Methods Used

 Standard deviation and regression analysis were used in the study (ibid. 327).  Standard deviation is a measure of the distance that a variable value is from a statistic- usually the mean, or average (Gomez & Jones 2010, 286-287).  Values below the mean are negative, while values above it are positive.  Standard deviation is the most common measure of interval/ratio data (ibid. 287), which is the type used in the study.

Regression analysis measures the strength of correlations between variables on a number line.  It is an equation of the line that best fits a scatterplot of the data (ibid. 301).  Measuring the predicted change in one variable (i.e. Y) as the value of another (i.e. X) changes is called the slope of the line (ibid. 302).  The slope, along with the Y-intercept, can then be used to predict values for which there isn’t any data on a graph.  Multiple variables can be used to evaluate the strength of their relationship to a common variable (ibid. 304-305), simply by adding their coefficients (slopes) to the equations.

In the study, secondary data from the U.S. Bureau of Economic Analysis was filtered to exclude age groups that aren’t typically in the job force (i.e. infants and elderly).  Cities were evaluated based on the frequency of standard deviations from the EMP/WAP ratio mean (Graves et al, 327).  The EMP/WAP is the employment to working age population ratio, which has historically been a useful predictor of population growth (ibid. 326).  The regression analysis was a test of the long-term EMP/WAP relationship between the 50 cities and the entire U.S. population mean (ibid. 327). 

 

Analysis and Discussion

            Using regression analysis, the researchers found a weakening in the linkage between job creation and population growth of U.S. cities (ibid. 330).  The last two decades (2000s and 2010s) showed stronger deviations than the previous three (1970s through 1990s).  The education level of citizens showed a strong correlation in cities with higher deviations above the mean (ibid. 329), indicating it was the best predictor of a city having more jobs per working age person.

            While support for the statement that people are no longer moving to urban areas for jobs is shown in the study, it wasn’t as successful at explaining the spatial patterns found.  It appeared from the map provided that northern cities were more likely to have higher deviations above the mean, with southern cities having lower ones.  Yet it is stated in the article that this diversity appears heterogenous (ibid. 330).  Providing statistics on this information would have helped explain it better, and supported the article’s title more since it mentions spatial change.

Statistical methods can provide powerful insights into the relationships between variables.  One drawback of using standard deviation is that it can be sensitive to extreme outliers (Gomez & Jones 2010, 287), particularly when the data set is small.  A problem with regression analysis is that a correlation isn’t the same as causation (Gomez & Jones 2010, 306).  Though a strong relationship may have been found, it’s possible that there was an even stronger variable(s) left out of the study that would have supported causation more. 

For instance, a big factor ignored in this study could be the percentage of jobs that are service-oriented as opposed to industry and manufacturing.  One change in the U.S. economy last century was the transition from a manufacturing economy to a service one (Harris, 2020).  With steady jobs in plants and factories being replaced by retail and hospitality positions, people were more likely to find part-time work, which was also exacerbated by recessions.  Service economy jobs also include professional ones in business and education (ibid.), which may be why the education finding was most significant.  If the regression analysis had included service jobs as a factor, it might have shown this as a strong relationship in spatial change between cities.

 

Conclusion

             Statistics is a good research method to use if the researchers are examining the right variables.  A conclusion can be falsely drawn if they are not.  Finding the right variable may even be the point, which is why multivariate regression is so useful.  On the chance a key variable is missed, the research won’t have as strong a conclusion.  In this study it wasn’t as important to find the why as much as it was answering the question, Are jobs still bringing people to cities?  Standard deviation and regression did answer this, but it leaves the reader wanting more.

Playground, Richard Powers

  "Playground" by Richard Powers has one of the best twists in modern literature. It is a subtle one that may be hard to realize at first. Spoilers: here is an explanation of the brilliant ending.

 Todd Keane has been writing letters to an AI machine that is in turn writing his life's story. Since he can't remember things from dementia, he has the machine write a story about how he would like to remember them, not necessarily how they were. For example, we are led to believe from the AI story that Ina and Rafi did not sleep with each other. At no point in the novel is it ever stated. Yet that is the only explanation for their abrupt rift, and is the reason Rafi no longer trusts Todd. Todd would like to forget this ever happened, and write a story about him that does not leave a bad impression on his readers. Every narrative in the novel that isn't written in italics is AI, while all the italics are actual letters from Todd to the machine to help generate a story.

 What is also fed to the machine is Evie's popular book with detailed descriptions of the marine world. Todd read it as a little boy, which inspired him to preserve the ocean. But this was a lost dream as greed propelled him into a career in tech. This regret is relieved in the AI story, as Todd saves the island his friends live on, with Evie also living there, who in reality is deceased like Rafi. This is why the book has so many beautiful, albeit quirky descriptions of the oceans. It really does read like AI in some parts, and I wouldn't be surprised if Powers actually used it, though highly doubtful- he would lose integrity over making a point. He is a gifted writer whose new book rivals "The Overstory" as his best. 

 The twist is that Rafi never lived on the island. He was dead the whole time. But the AI that Todd worked on his whole life resurrected him by changing history, as Rafi had once philosophized about the point of evolution. It's a rare book when so many loose ends are tied together at once. 

 This would make a great film, with many layered metaphors involving manta rays and the game of Go. It demands a re-read, at least for me.