My review of George du Maurier’s Trilby

By Graham

TrilbyTrilby by George du Maurier
My rating: 3 of 5 stars

It happened the other day, while I was reading George du Maurier’s Trilby, that a young man asked me whether I read mainly fiction or non-fiction – his preference clearly being for the latter. I answered the former, and had to supress within me a slight sense of shame. Does the fiction reader not, after all, sunbathe in supercillious fantasy while lazing on the beach; whereas the non-fiction reader applies his mind to the ‘hard facts’?

Maybe it is engrained in us to think so. But the distinction is shallow and meaningless when you dig a little deeper. For one thing, if 2020 has anything to teach us, it is that the ‘hard facts’, even those that are as hard as rock, are so numerous and tiny that they give way to the cudgel of dogma and zealotry, like grains of sand on that very same beach. One eye-catching event, propelled by the right algorithms, can trump an entire discipline of rigorous empiricism.

Non-fiction can easily fall into the trap of pretending the ‘castle of truth’ which the author has built up is structurally sound. Fiction, as written from the perspective of the narrator, or better still, the third persons who inhabit the narration, harbours no such pretense of architectural stability. The reader knows that the truth on which a novel is based is a shifty one; changing with the tide and giving way to the footprints left by the author’s own biases, those of his characters and those of the reader.

In this respect, a book like ‘Trilby’ helps us gain perspective on the ‘truthiness’ of our own age. It places fantastical events in a historical and subjective context, and in doing so removes us from the fantastical context of our own time, allowing us to regard these as no less subjective and ephemeral.

At the time of its publication, ‘Trilby’ was a sensation – the ‘Da Vinci Code’ of its day. Upon reading it, it’s easy to see why. Borrowing with self-effacing openness from Thackery, Dickens and Dumas, this festival of vanity, a tale set in Two Cities, chronicles the adventures of three very British ‘musketeers of the brush’ (artists) and their acquaintance with the Anglo-Irish Parisian washerwoman of the title. The narrative is light and fun, rich in the tradition of turn-of-the-Century satirists like Wilde or Saki. The plot is compelling, though perhaps somewhat too linear for modern tastes.

Mostly though, I read it as an antidote to the irrationality and illiberalism of the dominant ‘Liberal’ world view. If we must inhabit sand castles in order to have a coherent frame of reference, let’s at least decorate them with the colourful seashells of funny, well-written Victorian prose.

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 Category: Art Book Reviews

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As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:

fitted-death-rates-against-actual-values

While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

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We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values:

fitted-emissions-to-gdp-against-actual-values

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