知識の呪い（the curse of knowledge）とは一体何のことでしょうか。
The Curse of Knowledge: the Problem of Implicit Assumptions
One of the hardest things to learn when you’re an expert on some subject is that most people who read your papers know less about the subject than you do. This is called “the curse of knowledge” (https://en.wikipedia.org/wiki/Curse_of_knowledge), because the curse leads you to incorrectly assume that your readers already know most of what you’re describing. One reason why journals ask authors to spend so much time writing and reviewing the Methods section of their paper is that readers need to know exactly what you did so they can repeat your procedures exactly and obtain the same results (i.e., can replicate your research). Insufficiently clear descriptions are partially responsible for the “replication crisis” (https://en.wikipedia.org/wiki/Replication_crisis).
Note: The inability to replicate an experimental result also results from the complexity of the natural world, and particularly from our inability to completely control experimental conditions for living organisms and some inanimate (non-living) physical processes such as the weather. But in this article, I’ll focus on the curse of knowledge, since that’s something you can control.
Failure to ensure that readers know what they need to know so they can understand your writing leads to misunderstandings. This unexpressed knowledge often arises from “implicit assumptions”—assumptions that you have not made explicit by stating them clearly. These unwritten assumptions lead to an incomplete explanation of the context for your research, and thus, an incomplete understanding of the constraints of your research, including limitations on how it should be applied.
For example, consider the difference between using clonal plants (ramets) produced from a single individual and seeds collected from several populations of wild plants. The ramets offer the advantage of a single, consistent genotype that reduces the effect of genetic differences between individuals, but the experimental results are then limited to that genotype rather than being applicable to a population or the whole species; in contrast, seeds obtained from widely separated wild plants will have higher genetic variability, and therefore provide results that better predict the behavior of a diverse population of individuals, though at the cost of more variable responses to a given experimental treatment. Between these extremes are natural populations that have undergone natural selection that eliminated some of the population’s natural genetic variation, or populations that were established by only a few individuals (i.e., the “founder effect”). If you only state that you obtained plants from a nursery, readers cannot know which of these three types of plant you studied.
Two other problems make it essential for authors to identify and clearly explain their assumptions. The first problem relates to students. Students are still learning your field of research and therefore have large gaps in their knowledge. If you don’t explicitly state your assumptions, most students will never know what those assumptions are. Thus, there is a significant risk that they will incorrectly apply what they learn from you to situations for which that knowledge is inappropriate. Surprisingly, this problem is particularly serious for graduate students. By the time someone reaches graduate school, we can safely assume that they have learned a large amount about their chosen subject. But because one goal of graduate school is to teach students to work independently and to quickly understand complex subjects (e.g., journal articles) without any help, we incorrectly assume that graduate students will always succeed at this task.
Computer programming is a good example of why this assumption is unsafe. Some graduate students are fortunate to have taken a basic course in computer programming as an undergraduate that taught them the basic principles of software design. However, when a graduate student needs to create software, they typically spend a few days reading the user manual to learn the programming language they will use, and then begin writing their software. Often, the resulting software contains many significant errors, and the explanation is simple: professional programmers spend 3 or more years mastering the skills they need to create effective, reliable software. A graduate student cannot possibly achieve this expertise based on a few hours of undergraduate training or the few weeks they have available in graduate school to learn programming.
The second problem relates to readers for whom English is a second or third language and who are therefore not proficient in English. This means that in addition to having to understand the complexity of your field of research, they must also deal with the complexity of a language they have not yet mastered. We can assume that anyone who reads an English journal has at least a basic understanding of English, but we can’t assume they will be fluent. We also can’t assume that they come from an English-speaking country. Most English journals have an international audience, and readers may come from Asia, South America, or eastern Europe. Readers from these regions have learned their English from different sources, leading to different understandings.
The problem is exacerbated by the fact that different countries use words differently. For example, many Western readers believe that “gobi” is the name of a specific desert in northern China. In fact, Chinese desertification researchers use the word to mean any desert whose surface is composed primarily of fine and coarse gravels rather than sand. The area that Westerners traditionally call “the Gobi Desert” comprises several smaller deserts with distinctly different conditions, including parts of the Badain Jaran, Tengger, and Ulan Buh deserts. This kind of misunderstanding is why it’s necessary to define all terms in a manuscript that readers must understand correctly before they can understand your research.
The need to define your assumptions depends on the degree of specialization of the journal that will publish your manuscript. If you plan to publish in a specialist journal such as Protein Folding, you can assume that all readers will understand the specialized terminology of this field of biochemical research. In contrast, if you want to publish an article on protein folding in a generalist journal such as Nature or Science, you can assume that readers are smart and highly educated, but that they will not know the terminology that describes how proteins fold. You must concisely provide them with that understanding.
The best way to ensure that you have explained all the assumptions required for readers to understand your research is to ensure that all of your co-authors read the paper before you submit it for journal review. This is a formal journal requirement that many authors ignore. But you co-authors must do more than read a paper; they must also ensure that they understand it. If your co-authors don’t understand your writing, nobody outside your research group will understand it either—including a journal’s reviewers. When a reviewer fails to completely understand your paper, this can lead to long delays when they reject a manuscript or request major revision. Many of these rejections could have been eliminated by careful review of the paper by all co-authors.
However, if your co-authors are all experts in the subject of the paper, they will share many of your assumptions and will suffer from the same curse of knowledge. To solve that problem, ask colleagues from related fields of research to review your paper—or ask your graduate students. These people will have sufficient expertise to understand the basic ideas of your research, but not so much expertise that they share all of your assumptions. A skilled scientific editor can also help; such individuals offer a good balance between basic understanding of a wide range of subjects and ignorance of your specific research that lets them identify gaps in the information you present.
第4回 「～を用いて」の表現：by と with の違い
第6回 原因・結果を表す動詞の正しい使い方：その1 原因→結果
第7回 原因・結果を表す動詞の使い方：その2 結果→原因
第15回 所有を示すアポストロフィー + s ( ’s) の使い方
第18回 前置詞 of の使い方: Part 1
第19回 前置詞 of の使い方: Part 2
第22回 不定詞と動名詞: Part 1
第23回 不定詞と動名詞の使い分け: Part 2
第25回 総称表現 （a, theの使い方を含む）
第29回 then, however, therefore, for example など接続副詞の使い方
第30回 まちがえやすいusing, based onの使い方－分詞構文
第31回 比率や割合の表現（ratio, rate, proportion, percent, percentage）
第32回 英語論文の書き方 総集編
第33回 Quality Review Issue No. 23 report, show の時制について
第34回 Quality Review Issue No. 24 参考文献で日本語論文をどう記載すべきか
第35回 Quality Review Issue No. 25 略語を書き出すときによくある間違いとは？
第36回 Quality Review Issue No. 26 ％と℃の前にスペースを入れるかどうか
第37回 Quality Review Issue No. 27 同じ種類の名詞が続くとき冠詞は付けるべき？！
第38回 Quality Review Issue No. 22 日本人が特に間違えやすい副詞の使い方
第39回 Quality Review Issue No. 21 previous, preceding, earlierなどの表現のちがい
第40回 Quality Review Issue No. 20 using XX, by XXの表現の違い
第41回 Quality Review Issue No. 19 increase, rise, surgeなど動詞の選び方
第42回 Quality Review Issue No. 18 論文での受動態の使い方
第43回 Quality Review Issue No. 17 Compared with とCompared toの違いは？
第44回 Reported about, Approach toの前置詞は必要か？
第45回 Think, propose, suggest, consider, believeの使い分け
第46回 Quality Review Issue No. 14 Problematic prepositions scientific writing: by, through, and with -3つの前置詞について
第47回 Quality Review Issue No. 13 名詞を前から修飾する場合と後ろから修飾する場合
第48回 Quality Review Issue No. 13 単数用法のThey
第49回 Quality Review Issue No. 12 study, investigation, research の微妙なニュアンスのちがい
第50回 SinceとBecause 用法に違いはあるのか？
第51回 Figure 1とFig.1の使い分け
第53回 Quality Review Issue No. 8 By 2020とup to 2020の違い
第54回 Quality Review Issue No. 7 high-accuracy data? それとも High accurate data? 複合形容詞でのハイフンの使用