I am now nearly at the point where I actually begin editing the manuscript itself. I’ve created a stylesheet and cleaned the document (see The Business of Editing: The AAE Copyediting Roadmap II), and tagged the manuscript by typecoding or applying styles (see The Business of Editing: The AAE Copyediting Roadmap III), and inserted bookmarks for callouts and other things I noticed while tagging the manuscript (see The Business of Editing: The AAE Copyediting Roadmap IV). Now it is time to create the project- or client-specific Never Spell Word dataset and then run the Never Spell macro.
Never Spell Word (NSW) lets me create project- or client-specific datasets. If I know, for example, that the client prefers “distension” to “distention,” I can, using NSW mark every instance of “distension” with green highlighting, which tells me that this is the correct spelling, and change every instance of “distention” to “distension,” which change will be made with tracking on and then highlighted in cyan to visually clue me that a change has been made (I can choose to make the changes with tracking off, but that is not something I ever do).
Tip: It is important to remember that the tab names, such as “Drugs,” in the Never Spell Manager, and in nearly all managers, can be changed to whatever name best suits your editorial business. Use the Change Tab Name button. The tab names that show when you install EditTools are placeholder names.
Highlighting is integral to EditTools. Highlighting attracts the eye and by using different highlight colors, I can, at a glance, tell whether I need to review or check something. Because of the types of books I work on, it is not unusual for Word to put a red squiggle under a word or phrase that is actually correct — it just isn’t in Word’s dictionary. Most editors would stop and check the squiggled word, but, for example, if I see it is highlighted in green, I know that it is correct and I do not have to check it — I know I have already checked the word and then added it to a tab in the Never Spell Manager.
The point is that NSW enables me to mark (via highlighting) items that are correct, items that need to be checked, items that are correct but may not be capitalized correctly, items that should never be spelled out, and items that should always be spelled out according to the stylesheet and client instructions. Some examples are shown in the image below (you can make this image, as well as other images in this essay, larger by clicking on image):
The datasets are text files. The above image shows a project-specific dataset that was opened in Notepad++ (Notepad++ is an outstanding free text editor that is a replacement for Microsoft’s Notepad). The * and $ preceding an entry indicate case sensitive and whole word only, respectively. For example (#1 in image above),
*$ms | cyan -> msec
means: find instances of “ms” as a lowercase whole word (in other words, “ms” but not, e.g., “forms” or “MS”) and change it to “msec.” What I will see in the manuscript is this:
The cyan tells me at a glance that this has been changed by NSW. If the change is incorrect for some reason, I can reject the change, which is why I do it with tracking on.
I use NSW as a way to implement stylesheet decisions, as well as client preferences. An example is “F/M” (#2 in above image). The nice thing is that I do not need to format the entry. The Never Spell Manager, shown below, makes it easy — I just fill in the blanks, and if appropriate check one or both checkboxes, and click Add. I can easily correct an erroneous entry by double-clicking on it, correcting it, and clicking Update. And the Manager stays open and available until I click Close. With this Manager, I can make additions to any of the tabs.
I also use NSW as a way to mark things I already know are correct or incorrect and need changing so that I spend less doing spell checking tasks and more time doing higher-level editing. When I come across a new term, such as the name of a new organism, if appropriate I add it to one of the NSW datasets after I verify it so that next time it will be highlighted and, if necessary, corrected. For example, authors often type ASO3 rather than the correct AS03 (the first is the letter O then second is a zero). Having come across that mistake often, I added the instruction to change ASO3 to AS03 to my Commonly Misspelled Words dataset. Another example is the word towards. The correct spelling in American English is toward, so I added the word towards and the correction (toward) to an NSW dataset.
When I run the NSW macro, I am actually running more than what is contained in the Never Spell Words dataset — I can choose to run one, some, or all of the datasets represented by the tabs in the Never Spell Manager shown here:
In this example, I am running all of the datasets except the Confusables dataset.
Tip: Using only the datasets that are applicable to the project allows the NSW macro to complete faster. This is especially true as your datasets grow.
I run the NSW macro over the main text; I do not run it over the reference list. My habit is to move the reference list to its own document after I style/code and do cleanup, but before I run NSW. The NSW macro requires the placement of a bookmark called “refs” at the point in the manuscript where I want the macro to stop checking text. Consequently, I do not have to move the reference list to a separate file if the list is after the material I want the macro to go over — I can just put the bookmark in the reference list head or in a line that precedes the list. I move the reference list to its own file because my next step will be to run the Journals macro, and that macro works faster and better when the reference list is in its own file, especially if the dataset is large as mine are (e.g., my AMA style dataset runs more than 212,000 entries).
As I said earlier, I keep the Never Spell Manager (shown above) open while I edit. Doing so lets me add new material to the various datasets as I edit the manuscript. The idea of the multiple tabs is to be able to have specialized datasets that are usable for all (or most) projects; for me, only the Never Spell Words dataset is project/client specific. When I come across the name of a study, for example, such as AFFIRM (Atrial Fibrillation Follow-up Investigation of Rhythm Management), I enter the information in the Studies/Trial tab dataset, because that is information that is neither project nor client specific.
I also keep open the Toggle Managers because when I come across something like the AFFIRM study I want to enter it into the appropriate Toggle dataset, too. But the Toggle macro is the subject of a later Roadmap essay (The Business of Editing: The AAE Copyediting Roadmap VIII).
After running NSW, it is time to turn attention to the reference list. The Journals macro and the Wildcard Find and Replace macro are the subjects of The Business of Editing: The AAE Copyediting Roadmap VI.
Richard Adin, An American Editor