As I thought about this, I started getting reminiscent about a course I created for graduate students who needed familiarity with graduate school writing genres, so I did a needs analysis and found an incredibly useful textbook for them: John Swales and Christine Feak's Academic Writing for Graduate Students. The book has chapters on writing summaries, data commentaries, critiques, problem-process-solution texts, and general-specific texts, as well as an introduction to writing IMRAD articles. Most of all though, I appreciate the first unit, which is "an approach to academic writing" (after we got through the introduction, I would revisit the unit's title and point out how even the title displays the cautious tone of academic writing since it is not "Academic Writing" but "An Approach to Academic Writing" (emphasis added)).
There is an invaluable activity in the first unit called "Gene's Conclusion" (which is renamed "Sam's Conclusion" in the third edition for some reason). The activity describes a student who writes up an effective conclusion, but then realizes that there are limitations and weaknesses in his data set that he has not described anywhere else in his paper. Should he acknowledge these in the conclusion or should he sweep them under the rug? I love this activity because in the four semesters I taught this class, it never failed to generate a lot of discussion. After a short time, students usually broke into three schools of thought:
School #1: The Choir
School #2: The Overachievers
"If you find problems, you must go back and fix it."
"But you don't have any money. You used up all your funding to get the data you have."
"Um... well, you need to get more money."
"You can't do that. The publishing deadline is coming up soon. You're under the gun."
The silence becomes too uncomfortable and usually leads students to defect to...
School #3: The Deniers
"Don't do it. You should never talk about your weaknesses or problems."
"Yes. It will be bad. It's better not to mention it."
"But don't you think it will look more thoughtful and meticulous to note the limitations of your data?"
"No. It's better not to mention it. Maybe no one will notice."
"But suppose someone does notice, what then?"
"So I should point out my own mistakes? Then what will I look like? People will see me and think, 'He is stupid!'"
"No, no, they won't think you're stupid. It will be the opposite effect, actually."
Then I tell them a story about a AAAL conference I attended back in 2011 in Chicago. There was a man who presented an experimental treatment that his data seemed to support as effective. He finished his presentation and began fielding questions.
"Could you go back to the slide with the results on it?" an astute woman in the back asked. He flipped back in the PowerPoint to a slide that looked like this: