In-class writing exercises

Every class session will begin with a brief in-class writing exercise. These exercises are meant to help you practice and hone your ability to write clearly, concisely, and appropriately for your intended audience. You will work on these individually at the beginning of class for the designated time, and submit them via Canvas EEE by the designated time in class.

Submissions are accepted as on-time through 11:59pm PST the day the in-class exercise is due.

Late submissions are accepted until 2pm PST the Friday of Week 10. Please email the professor if you submit a late writing exercise, to make sure you receive credit for it.

These exercises will be graded on a 5 point scale, and primarily graded for effort, as they're meant for practice.
  • 5 points: On-time submission where you put in a good effort
  • 3 points: Late submission where you put in a good effort
  • 0 points: Otherwise :(

Attendance

Being "present" in class has several benefits:

  • You hear additional information relevant for the in-class exercises and the assignments you're working on.
  • You have a chance to ask the professor and your classmates for help on things you're currently stuck on.
  • You have dedicated time to make progress on your assignments.

When the class is offered remotely (either only remotely or in a hybrid format), the following policy applies because life can interfere sometimes:

The active part of the class sessions will be recorded (i.e., discussions of material, but not working on assignments). The recordings of each class session will then be available on the Canvas coursesite.

If you were able to attend class in real time, you don't need to do anything.

If you weren't able to attend class in real time, please do the following:
  • look over the in-class exercise for the day
  • post your own reply to the in-class exercise
  • like any in-class exercise replies you thought were good
  • watch the linked recording of the active part of the class session
  • and then reply to that session's attendance assignment with a message like "I did the
    in-class exercise and watched the recording"


This is of course on the honor system. Be honest. It's good for you.
You'll receive 1 point for each class session attended either synchronously in real time or asynchronously by viewing that class's session.

Latex exercises

These exercises are designed for you to practice formatting and typesetting using the Latex software available through overleaf. There are several throughout the quarter, with time in class to work on them. Late submissions are accepted through 11:59pm PST the day the latex exercise is due.

These exercises will be graded on a 5 point scale, and primarily graded for effort, as they're meant for practice.
  • 5 points: On-time submission with no serious mistakes
  • 4 points: Late submission with no serious mistakes
  • 3 points: On-time submission with serious mistakes
  • 2 points: Late submission with serious mistakes
  • 0 points: Otherwise :(

Peer review

Peer review (the primary form of feedback we get in science) is an important component of scientific writing -- both receiving peer reviews and generating helpful peer reviews. For every abstract draft you generate, there will be a peer review component -- this means you need to have a draft of your abstract ready for review at the appropriate time, and you need to generate helpful peer reviews. In this class, that means having your draft ready the day we're doing peer reviews in class, and generating peer reviews for your classmates to use when revising their abstracts. Because peer reviews are used by your peers to revise their abstracts, no late peer reviews will count for credit.

Abstract drafts for peer review:
  • 5 points: On-time submission where you put in a good effort
  • 3 points: On-time submission where there's still enough there to peer review
  • 0 points: Otherwise :(
Note: Informative peer reviews generally start with an overall comment about the quality of the draft, and then separate sections for major comments (general issues/observations, suggestions for organization, etc.) and minor comments (specific questions/observations about one part, typos, etc.).

Peer reviews (each one -- you'll typically do two per session):
  • 5 points: On-time peer review where you put in a good effort
    (or your assigned draft wasn't available)
  • 0 points: Otherwise :(

Revised abstract drafts

Once you have your peer reviews, you will then try to incorporate that feedback into a revised abstract draft. Revised draft submissions are accepted as on-time at the beginning of class the day the revised abstract is due, with late submissions accepted through 11:59pm PST the day the revised abstract is due). Because these are still drafts, they're graded primarily on your effort to incorporate the comments. Your revised draft should highlight (color-code usually) the parts of your draft that you updated based on your peer reviews.

Revised abstract drafts:
  • 5 points: On-time submission where you put in a good effort
    and highlighted the parts you updated
  • 4 points: Late submission where you put in a good effort
    and highlighted the parts you updated
  • 3 points: On-time submission where your effort was not-so-good
    and/or you didn't highlight what you updated
  • 2 points: Late submission where your effort was not-so-good
    and/or you didn't highlight what you updated
  • 0 points: Otherwise :(

Midterms: Re-revised abstracts

Twice during the quarter, you'll submit a re-revised abstract. This will build off one of the revised abstract drafts you've already submitted, incorporating your growing knowledge about how to write well about language science (and also allowing you to experience the iterative cycle of revision involved in science writing). These serve in place of midterms, and will be graded more stringently according to the rubric below. Late submissions will be accepted through 11:59pm PST the day the re-revised abstract is due for a 10% penalty (-10 points off the top).

Re-revised abstracts:
  • First paragraph:
    • Context for main question & main question itself (10 pts)
    • Methods to answer question (5 pts)
    • Results summary (10 pts)
  • Other paragraphs:
    • Relevant background (by other people) (10 pts)
    • Specific details about your approach & analysis (10 pts)
    • Specific details about your results (10 pts)
    • Interpretation of results (10 pts)
    • Connection to larger context of language science and/or future extensions
      (10 pts)
  • Style:
    • Lexical choice (5 pts)
    • Sentence structure (10 pts)
    • Tailored to intended audience (5 pts)
    • Within abstract length limit & obeying formatting guidelines (5 pts)

Final abstract

Your final abstract will be generated based on a paper or project we haven't spent time on in class, and will be one you choose yourself. You'll draw on your science writing skills to generate a draft, incorporate feedback from peer reviews, and produce a final abstract targeted to a specific audience. Late submissions will be accepted through 11:59pm PST the day the final abstract is due for a 10% penalty (-10 points off the top).

The same more-stringent grading rubric used for the midterm abstracts will be used for the final abstract.

Extra credit

Extra credit can be earned on a per-assignment basis by helping answer your classmates' questions, using the discussion thread on the message board dedicated to questions about class assignments.

You will receive extra credit on the assignment for every student who indicates you helped them on that assignment.

If someone helps you on your assignment, please indicate who it was and how they helped you as a comment when you turn your assignment in.

Using AI

You're welcome to use AI in this class (e.g., ChatGPT, Dall-e-2, anything else that crosses your path that seems useful, etc). Learning to use AI is an emerging skill that can help you improve your workflow if you can figure out how to use it effectively. To use AI effectively, you need to be aware of the limits of these software systems.

In particular, AI is a tool, just like a pencil or a computer. AI can be a valuable tool for drafting content and fine-tuning content that's already been produced, but it definitely isn't a replacement for critical thinking and decision-making.

For instance, if you provide minimum-effort prompts, you'll likely get low-quality results. You'll need to refine your prompts to get better outcomes. This will take time and practice, as well as your own knowledge of the content.

In terms of content, assume what the system generates is probably wrong, unless you already know the answer and can verify with trusted sources. It works best for topics you deeply understand. This is why tailoring your prompts can be very helpful.

Use your best judgment to determine if/where/when to use this tool. It doesn't always make your life easier and/or better (but sometimes it really does).

Academic dishonesty

Academic integrity is vital for successful learning. Seriously. Please don't be academically dishonest. It's painful for all of us. Please speak to the professor if you have any questions about what is and is not allowed in this course.

Academic dishonesty includes cheating on any assignment, having someone else complete an assignment for you (or doing this for someone else), copying someone else's assignment, and any activity in which you represent someone else's work as your own.

If you are caught being academically dishonest, you will receive a 0 for the assignment and you will be reported for academic dishonesty at the very least. Additional action may be taken, depending on the nature of the incident. Please see the information about academic integrity here, and more about what it means to be academically dishonest.