0 INTRODUCTION

6. FINDINGS
(144) Some years after the making the Experiments about the
Production of Electricity, having a desire to try, whether in the
Attractions made by Amber, the motions excited by the air had a
considerable Interest, or whether the Effect were not due rather to
the Emission and Retraction of Effluvia, which being of a viscous
nature may consist of Particles either branch’d or hookt, or
otherwise fit for some kind of Cohesion, and capable of being
stretch’d, and of shrinking again, as Leather Thongs are : To
examine this, I say, I thought the fittest way, if $’t $were
{TEXT:’twere} practicable, would be, to try, whether Amber would
draw a light Body in a Glass whence the air was pumpt out. (E3
1675-6 Boyle Electricity & magnetism)
(145) Also, it is auailable to vse Frictions, Rubbings, Borings, and
Blisterings is much praised after purgings, for it stoppeth the
flowing matter being applyed vpon the head by revulsion or
drawing back, & causeth euacuation. (E2 1602 Clowes Treatise
for the artificall cure of struma)
The figures for the occurrences of nominalizations in the corpus in the three
subperiods under analysis are shown in Table 8. It must be noted that frequencies have
been normalized following Biber’s (1988: 14) proposal for a “normalised frequency of a
feature,” as the data analyzed in the different periods differ in size (see Chapter 5). As
Biber notes, “raw frequency counts cannot be used for comparison across texts when they
are not at all of the same length,” since in this case longer texts would tend to have higher
frequencies simply because a given feature has more opportunities to occur in them. Using
Biber’s procedure and comparing the frequency per 100,000 words or any multiple of 10 –
depending on the frequency of the feature under investigation– this possible bias is
eliminated. In the present study, given that nominalizations are considered relatively
frequent constructions, normalized frequencies are computed by 10,000 words.
Normalized frequencies are achieved by dividing absolute frequencies between the total
number of words of each category; the total is then multiplied by 10,000. Hence, for
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