When reviewers read what they assume, not what you wrote
Most academics know the feeling. You spend days – sometimes weeks – carefully crafting a funding application. Every sentence is considered, every unnecessary word removed. You think hard about scope, feasibility, training value and impact. You anticipate concerns before they arise, make sure the methods are realistic, confirm that support structures are in place, and try, above all else, to be honest about what the project will actually involve.
And then the reviewer feedback arrives.
Sometimes it is insightful, fair and genuinely useful. Sometimes, however, it leaves you staring at the page in quiet disbelief – not because the proposal was criticised, but because the criticism seems to address a version of the proposal that was never actually written.
A mismatch in interpretation
This is one of the more frustrating features of academic peer review: the gap between what is on the page and what a reviewer understands to be there. A principal investigator mentions direct supervision and regular one-to-one meetings; feedback notes that student support is unclear. A proposal outlines opportunities for presentations and scientific discussion; reviewers observe a lack of communication training. A carefully integrated workflow – designed so that each step builds logically on the previous one – is read as an overly ambitious collection of disconnected objectives.
These are not disagreements over scientific merit. They are mismatches in interpretation. And that distinction matters, because the two require very different responses.
Scientific criticism is essential. Good review sharpens ideas, exposes weaknesses and improves projects. Critique built on assumption rather than close reading is a different thing entirely. It risks rewarding applications that are broad in promise but vague in detail, while penalising those that are specific, carefully planned and honest about what will actually be done.
A strange incentive
Applicants learn this quickly. Clarity can be a liability if detail is read as complexity. Honesty about project scope can look less attractive than a simplified narrative designed to tick expected boxes. A training plan that exposes students to several connected techniques may be criticised for lack of focus, while a more polished but less realistic proposal may appear stronger simply because it leaves more to the imagination.
Put plainly: sometimes the best grant writing is not the clearest writing, but the writing that best anticipates reviewer assumptions. That should give us some pause.
On the reviewer's side
Peer review is difficult work, carried out by busy scientists giving their time voluntarily. Nobody reads an application with malicious intent. But this is precisely why careful reading matters. When we evaluate proposals – particularly those involving student projects – it is worth being vigilant about separating what is explicitly stated from what we assume must be the case.
The useful question is: what has the applicant actually proposed, and what evidence have they provided? Not: what do I suspect lies behind this, or what usually happens in labs like this one?
A reasonable expectation
Academic funding is competitive, and rejection is part of the process. That is not in question. The problem is narrower than that: when a proposal is declined not because of weaknesses in what was written, but because of assumptions layered on top of it by the reader, the feedback tells the applicant very little they can usefully act on.
Scientists spend their careers designing experiments to challenge assumptions. It seems reasonable to ask the same of the review process.