Please Revise and Resubmit: Amazon’s Lesson for Academia

Hello Amazon Customer,

We couldn’t post your review because it focuses on one or more of these topics:

Sellers

Delivery

Packaging

Pricing

Availability

Many of us who use Amazon pay attention to reviews to help us decide which product among multiple options is most likely to do the job we have in mind. Amazon understands and pays attention to reviews—for our own good. With the dawn of language machines, Amazon now has the technological infrastructure to crack down on hapless reviews and nefarious reviewers.

Not just any review makes it through their assessment process. New technology has helped the company sift the good reviews from the bad and reject those that don’t serve the customer. First on Amazon’s list of priorities is customer satisfaction.

Like good businessmen everywhere, Amazon’s executives have plenty of legitimate concerns to keep them up at night. Incentivized reviews—when products are given for free or discounted in exchange for reviews—corrupt the signal even when the reviewer genuinely likes the product. The reviewer's judgment is compromised by reciprocity bias. Amazon banned most incentivized reviews in 2016, but they persist through underground networks bent on gaming the system.

Competitor sabotage runs the other direction as well. Sellers post fake negative reviews on rivals' products. One-star floods timed to product launches can tank visibility before legitimate customers ever see the item. This review strategy must rankle the executives more than any other with its hint of cannibalism.

Coordinated review networks and self-dealing are the industrial version, illicit review-writing strategies to amplify the voices of crooks. One study¹ found that fake review buyers cluster in network spaces. A single reviewer posting across dozens of unrelated products signals participation in a review-for-hire scheme. Amazon can't catch this dishonesty through text analysis alone; the reviews read fine according to the rubric. The fraud is structural.

Irrelevance covers reviews that have nothing to do with the product, such as political commentary, personal grievances, jokes without evaluative content.

Unintelligible content, whether from poor machine translation, incoherence, or insufficient information, doesn't help future customers.

It’s not easy for the company to monitor and assess zillions of written reviews. Anyone could understand why the company might want to chuck the whole approach and rely just on hearts or stars or thumbs up.

A technical problem for Amazon is the use of valid categories to build automated filters that overblock. "Mentions seller" catches both manipulation ("seller paid me to write this") and legitimate complaint ("seller sent wrong item"). "Promotional content" catches both spam and genuine enthusiasm.

The categories are defensible; the implementation is crude; but the crudeness serves Amazon's interest in volume over quality. After all, without volume, what does customer satisfaction even matter? We can all empathize with Amazon’s quality concerns and with its frustrations.

Amazon's standard automated rejection notice can feel like a slap in the face, and its customer service personnel may need counseling to absorb the emotional heat coming from customers who really wanted to get out the word about this particular revolutionary compound for removing age spots.

Please edit and resubmit your review. Is that so hard? If you cared enough, you would work with us. Before you do, make sure it meets all of our community guidelines.

There are logistics to consider. You try running Amazon if you think logistics aren’t important.

• Seller feedback: Reject. They have a separate "seller feedback" system for rating individual merchants

• Delivery/packaging: Reject. These reflect Amazon's or the seller's shipping, not the product. Nobody needs to know about these things to decide on a product.

• Pricing: Reject. Prices fluctuate; "great deal" or "overpriced" doesn’t age well.

• Availability: Reject. Stock issues are temporary and irrelevant to product quality

If you want your review posted, you'll need to edit out whatever triggered the filter, even if it was just a brief aside, and resubmit. Many reviews pass through the filters on a second try. You can always resubmit again. There is no limit.

Amazon has a mastery system of grading—reviewers learn to do better through feedback.

If this sounds familiar, it’s because another institution has perfected a similar system of polite, industrial rejection: the English Department.

A Note from Your Writing Instructor

Hello English Student,

We couldn't accept your essay because it focuses on one or more of these concerns:

• Personal opinion unsupported by textual evidence

• Emotional response to the material

• Questions rather than thesis statements

• Ideas not derived from the source texts

• Organizational structures not covered in class

Many of us who teach composition pay attention to rubrics to help us decide which essays among multiple submissions demonstrate the skills we have in mind. The Department understands the importance of clear criteria—for your own good. With the advent of standards-based assessment and LLMs, schools now have the pedagogical infrastructure to identify essays that miss the mark and students who need additional support.

Not just any essay earns a passing grade. Rigorous criteria have helped us sift the proficient from the developing and redirect those who haven't yet mastered the form. First on the Department's list of priorities is student success. What we do comes from a regard for your future, a motive exemplified by the thousands of hours we've spent aligning our instruction with college and career readiness standards so that you'll be prepared for the world Amazon is building.

Like good educators everywhere, your teachers have plenty of legitimate concerns. Unsupported assertions—when students offer opinions without grounding them in textual evidence—corrupt the analytical signal whether or not the student genuinely understands the material. The student's judgment cannot be verified through mere assertion. We banned unsupported claims in the revised curriculum guide, but they persist through underground thinking bent on self-expression.

Off-topic tangents run in another direction. Students insert irrelevant personal experiences into literary analysis. Anecdotal floods can derail an argument before the thesis ever develops. This compositional strategy troubles us more than any other with its hint of self-indulgence.

Undisciplined brainstorming is the amateur version, a strategy that mistakes quantity of ideas for quality of thought. Research has shown that unfocused prewriting produces unfocused drafts. A single essay containing dozens of unrelated observations signals participation in a write-whatever-occurs-to-you scheme. We can't catch this through surface assessment alone; the sentences read fine according to the grammar rubric. The confusion is structural; LLMs are adept at monitoring this feature.

Self-expression covers students writing about their own feelings, exploring their own questions, and developing their own frameworks. All compromise the analytical objectivity that makes academic writing valuable for legitimate readers who just want to know how this novel depicts class conflict or how that poem employs extended metaphor.

Plagiarism creates institutional exposure as well. An essay containing unattributed ideas or phrases from sources can generate academic integrity violations. Of course, the school has its own interest in not certifying fraudulent work.

For example, citation complaints arise when essays misformat references or when students use AI tools to generate content they claim as their own. Intellectual property violations occur when students submit work completed for other classes or by other people.

Irrelevance covers essays that have nothing to do with the prompt—personal narratives in response to analytical questions, creative flourishes unrelated to the assigned text, humor without argumentative content. Padding and filler include the same point repeated across multiple paragraphs to meet word-count requirements, sentences that are just throat-clearing before the actual claim, and AI-generated content.

Incoherent organization whether from inadequate outlining, stream-of-consciousness drafting, or insufficient attention to transitions doesn't demonstrate the skills future professors and employers require.

It's not easy for teachers to assess hundreds of student essays. Anyone could understand why we might want to chuck the whole approach and rely just on multiple choice tests. But we don't, out of respect for the student. And now we have LLMs to relieve the burden. It’s all about the public good.

We do our level best to be fair and accurate, but the problem is that rubrics use valid categories to build assessment filters that undervalue certain moves. "Lacks textual evidence" catches both lazy assertion ("I think the character was sad") and sophisticated synthesis ("The accumulating weight of these small betrayals suggests..."). "Unclear thesis" catches both genuine confusion and productive ambiguity.

The categories are defensible; the implementation is standardized; and the standardization serves the institution's interest in efficiency over depth. After all, without efficiency, what does student success or even learning outcomes matter? We can all empathize with students' creative impulses and their frustrations, but living in the world as it is requires adjustments.

The Department's standard rubric feedback can feel like a dismissal of your ideas, but more and more the LLM generates the feedback, and there is nothing personal about it. Your teachers probably need professional development to absorb the emotional labor of explaining the same criteria repeatedly to students who really wanted to explore this particular insight about the human condition.

Nonetheless, essays that stray into territory we consider outside the scope of the assigned task must be marked accordingly.

Please revise and resubmit your draft. Is that so hard? If you cared enough about your grade, you would work with us. Before you do, make sure it meets all of our assignment guidelines.

What we want in an academic essay is simple. Does it have a clear thesis? Is it supported by evidence? Does it follow the organizational template? Would a college accept it?

In addition to the struggling students who cause so many grading complications, there are logistics to consider. You try teaching five sections of thirty students each if you think logistics aren't important.

Personal voice: Reject. We have a separate "creative writing" elective for personal expression.

Process struggles: Reject. Your drafting difficulties reflect your work habits, not your analytical abilities. Nobody needs to know about your process to assess your product.

Evolving ideas: Reject. Arguments should be settled before you write; "I'm not sure what I think yet" doesn't demonstrate mastery.

Questions: Reject. Questions are for discussion; essays require answers.

Unconventional structure: Reject. Alternative organizations are temporary experiments and irrelevant to academic readiness.

The policy makes sense in principle, though rubric application can be inconsistent. According to sources, sometimes a passing moment of genuine voice or an unexpected structural choice triggers point deductions even when the essay substantively engages with the text.

If you want full credit, you'll need to revise out whatever triggered the rubric, even if it was your most interesting idea, and resubmit by the deadline. There is no grade penalty for using the writing center if your school has one. Alternatively, if your real interest was in exploring your own questions rather than answering ours, you can pursue independent study through the Gifted and Talented office instead.

In the future, no one will be rejected by humans, only by LLM filters calibrated to protect us from ourselves. And each polite rejection will remind us that behind every algorithm, there used to be a teacher, a reader, or a fellow shopper who once knew how to listen.

1. He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. PNAS, 119(47), e2211932119. https://doi.org/10.1073/pnas.2211932119