Page 233 - DLIS402_INFORMATION_ANALYSIS_AND_REPACKAGING

Information Analysis and Repackaging

Notes on the same fields as the books that they index. Indexers can keep in mind that any given book has
a spectrum of users. A technical book that is aimed at professional computer programmers, for
instance, may also be referred to by student, academic, or hobby programmers. A professional
programmer might look up a function by its known name, where as a student may be looking for a
function that fulfills a role. Both options for look-up should be present for important functions.
Importance is the main criteria for helping the user. But there is no doubt that users sometimes look
up trivia. If the length of the index allows for it, certainly trivia can be indexed. But to index trivia
rather than deeply indexing important subjects is a mistake.
Machine generated indexes, in their present state, are more helpful to users than having no index at
all. Amateur created indexes (usually by the author of the book) are usually at least as useful as
machine-generated ones. If the amateur knows the subject materials but not professional indexing
style rules, their mistakes tend to be largely stylistic. If a professional indexer does not understand
the material being indexed, the indexing errors tend to concern the choices of entries. Professionally
produced indexes are usually far better than machine-generated or amateur-created indexes. “Better”
here means that users can find the information they seek with minimal effort.

Author/Indexer/User Knowledge Relationships

Non-fiction book authors are consciously trying to convey a body of knowledge to the readers of
their books. The author and indexer each enter the enterprise with a body of knowledge. Their
knowledge bases may or may not be very similar. At minimum both the author and indexer are
proficient in the language used for the book. We might expect, therefor, that a machine indexer
must be proficient in the language to do a good job of indexing. The author has a body of knowledge
which he distills [note MI’s: example of metaphorical use of “distill”] into book form.

The indexer also has a body of knowledge. Reading the book (which would be impossible without
a pre-existing body of knowledge) the indexer, like other readers, learns what the author conveys.
It may be a particular arrangement of knowledge the indexer already has, but typically the indexer
does some, perhaps a lot, of learning while indexing. The index reflects both the knowledge of the
author and of the indexer. The author does not have to know how to create an index as he writes the
book.
Both the author and the indexer have a knowledge of the knowledge likely to be present already in
the book’s target readers (and index users). These readers might fall into classes: students with no
prior knowledge for whom the book is course work; workers in the field who use it only for reference;
etc., depending on the book.
If the author has written an introductory text aimed at initially ignorant readers, it is possible that
the indexer may do a good job without knowing much subject matter. If the author has written a
more advanced text, an indexer with no knowledge of the subject matter is likely to produce a poor
(not that helpful to users) index. Often the indexer will have a good general knowledge of a subject
such as computer science, and will be indexing a book on a relatively narrow subject (say C++
programming, or graphics algorithms).
Given that poor indexes are produced by human indexers who do not understand the subject matter,
we might expect that an MI will produce a poor index if it does not understand the subject matter.
“Understand” is, admittedly, a difficult to define precisely. We can also say the MI needs to begin
with a knowledge base similar to a human indexers, and needs to be able to learn (add to its
knowledge) just as a human indexer does.
A professional indexer who indexes a book on a subject that is similar to a prior book they indexed
will have a general index framework in mind. Such an indexer might think thoughts such as “ahah,
my first entry on the subject of Internet browsers,” or “oh no, she’s writing about event delegates,
I’ve had trouble understanding that in the past.”

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