Chemistry Reference
In-Depth Information
At various points in the research cycle, as researchers inevitably encounter results
that are somewhat different than expected, they will alter the
ceteris paribus
conditions, the explanatory model, the selection of relevant theories, the boundary
conditions of the phenomena under scrutiny, even the standard operating proce-
dures of their laboratory equipment. Essentially anything, whether theoretical or
instrumental, that can be bent to its near-breaking point will be bent; theory and
model will be made to accommodate instruments and experiment design and vice
versa, until expectations of acceptable experimental outcomes closely match actual
experimental outcomes.
Graves
'
analysis of the rhetoric of invention in MacDonald
'
s descriptions of his
research team
s understanding
of the mangle of practice. MacDonald and his co-workers undertook to modify
standard solid state physics models in order to fit the diverse experimental settings
for which they were not originally designed; in some cases, modifications were
stimulated by peer-review comments accompanying the rejection of their article for
publication in a journal.
s model-building efforts is consistent with Pickering
'
'
9.5 Metaphor in Chemistry Education
Nalini Bhushan and Stuart Rosenfeld
s 1995 article, “Metaphorical Models in
Chemistry,” offers an analysis of metaphor in reference to scientific modeling in
the service of pedagogy in chemical education. The authors cite James Hofmann
'
s
'
(
1990
) study of “How the Models of Chemistry Vie,” a play on Nancy Cartwright
s
(
1983
)
How the Laws of Physics Lie
. Hoffmann distinguishes two functions of
models in chemistry as “the culmination of phenomenology and the commence-
ment of explanation” (Hofmann
1990
, 406). The former offers “specific causal
scenarios,” while the latter presents “unifying explanatory formalisms.” Oddly,
Bhushan and Rosenfeld substitute these functions with the not exactly equivalent
pairing of “predictive” vs. “insightful.” The derivation of causal scenarios from
predictive ones, and explanatory formalisms from insightful ones requires clarifi-
cation. In the context of their discussion, Bhushan and Rosenfeld note that
“[a] working view for students might be that models should be seen as tools for
prediction and correlation but that one should remain aware of their metaphorical
standing.” (Bhushan and Rosenfeld 1995, 579). An understanding of the “standing”
of models as metaphorical is sufficient to arm the student against swallowing the
model whole, as it were, and taking it as literal truth. Bhushan and Rosenfeld
consider this to be good pedagogy. Furthermore, they note that
both
aspects,
prediction and insight, are metaphorical, since it is “not so odd to view computa-
tional models as metaphorical” [581].
Though they don
'
t say so explicitly, their pedagogy elucidates the metaphorical
nature of models in order to disabuse students from habitually thinking of models
as representations. Bhushan and Rosenfeld
'
s pedagogical ideals seem consistent
with thinking about models as interventions rather than literal descriptions.
'
