What, When, Why & Context


Gail & Tom Madden <tgmadden@...>
 

John's excellent post brings up several points. First, despite my flip sig
yesterday about cutting things off at the McGinnis interface (which would
leave Garth on the outside looking in), I think it's very appropriate to use
a rubber ruler when setting a cut-off date for the Steam Era. After all,
much of the rolling stock we're interested in, if not the motive power,
operated long after 1960.

Second, John's point about understanding the historical context for our
modeling is an important one. We can all build accurate models (the "what"),
and equip and weather them to represent a specific time ("when"), but for
many, that's where it ends. There's still the "why" - and for this you need
to understand the constraints and practices (both social and technological)
of the times - the context.

An illustration (and please forgive me for using a non-freight example, but
most of you know where my interests and expertise are these days). What: the
second and seventh rivets (bolts/screws, actually) from the bottom in the
rows adjacent to all full-height windows on heavyweight Pullmans stick out
further than all the other rivets in those rows. When: from when the cars
were built up to the present. Why: to hold wind/cinder/soot deflectors when
the windows were opened. Context: before air conditioning, porters were
instructed to place these deflectors on the forward edge of every window in
occupied space - seasonally, of course. More "what" and "when": the first
widespread application of air conditioning in the very early thirties was to
diners. Context: the public wasn't used to air conditioning (in 1935 Pullman
issued a brochure pleading with passengers to leave the windows closed on
air conditioned cars), so coolness wasn't yet a huge consideration - but
cleanliness was. Offering soot- and cinder-free dining was a great marketing
advantage.

Passenger mode <OFF>

There must be many equivalent examples in the freight car world, and the
point is that appreciating context can lead to insights that will let us
build even more accurate models.

Tom Madden

tgmadden@...

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