学术研究中的激励措施
Incentives in Academic Research

原始链接: https://www.msoos.org/2026/10/incentives-in-academic-research/

作者认为,学术研究的激励机制已经偏离其核心目标:推进真理、培养严谨的研究者和维护科学诚信。发表、晋升和名望可能形成强大的激励,使人们倾向于保护错误或误导性的工作,而不是承认缺陷、撤回主张或告知科学界。 作者对近期一些研究者抵制批评的实例感到担忧,并将他们与自己职业生涯早期的经历作对比:那时发现错误会令人尴尬,却也会立即促使人们加以纠正。他们担心,新一代博士生正在学习一种文化,在这种文化中,精确、好奇心和良好的科学操守不如职业成功重要。 核心信息是,研究应由诚实、责任和集体理解来指导,而不是由个人声誉来支配。机构必须奖励透明、谨慎评估、纠正错误以及对未来研究者的负责任培养;否则 declining standards 将继续损害科学和下一代。

《黑客新闻》的一个讨论帖引用了一篇文章。该文章认为,学术激励——包括论文数量、引用次数、经费、声誉以及“要么发表、要么淘汰”的机制——往往奖励的是关注度和职业晋升,而非真理、严谨性或可重复性。作者描述了疑似 p 值操纵和假阳性结果的问题,并指出,可疑论文可能始终未被撤回,而更正论文反而可能进一步奖励有缺陷的研究。 评论者普遍认同这一问题,但批评这篇文章缺乏深入分析和具体改革方案。有人建议奖励学术诚信和精益求精,改善同行评审及论文发表后的问责机制,并为研究人员提供资金,而不是让他们追逐可量化的产出。另一些人则强调,科学高度依赖信任,因此很难进行可靠、客观的评价。 不少参与者将这些激励问题与资助机构、大学、声誉网络联系起来,并认为 increasingly,AI 生成的“灌水”论文也在利用自动化审稿机制获利。讨论围绕这类工作究竟只是粗制滥造,还是实质上构成欺诈展开;同时也有人指出,现代的重复性研究工具和线上监督也可能改善科学。总体而言,这场讨论呼吁进行系统性改革,使可信知识优先于地位和数量。
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原文

Charlie Munger said: “Show me the incentive and I will show you the outcome”. The issue with Academic Research, in my opinion, is that the outcomes have drifted very far away from the original goal, which I believe to be the advancement of scientific understanding, and training of the new generation of researchers. Academic research was meant to be about breaking new ground, keeping to honesty and good scientific conduct, being clear and upfront about uncertainties, errors, and mistakes, and improving our common understanding of science, all the while training the new generation to follow these goals and principles.

Recently, I have bumped into multiple cases where I believe the correctness of the results, the honesty of the people writing them, or the lack of curiosity once they are told that their results are wrong, incorrect, faulty, or misleading, has been unsatisfying. Simply put, researchers are not too interested in learning that their papers or reports are wrong, and/or misleading others who don’t happen to know that the results are — known to the authors, and a few select others to be partially — incorrect.

The issue is, once you published the paper, and got the promotions and fame, it doesn’t matter that the results are wrong, and known to be wrong or misleading, and potentially doing harm to the advancement of science. It’s not your problem. It’s someone else’s problem. In fact, when I challenged the authors of one such paper, considered state-of-the-art, and known to the authors to have incorrect evaluation, one of the author’s response was along the lines of acknowledging the issues, but refusing to retract the paper, and instead asking if it’s bothering me in publishing my paper.

The incentives are wrong. In my opinion, it’s not all about the papers — mine or others’. It’s about scientific integrity, about being honest with each other, it’s about caring about the results, it’s about advancement of our common scientific understanding through seeking of truth and correctness. It’s about communicating when something is wildly wrong, and either retracting the relevant incorrect claims, or notifying the community of the known serious issues. It’s about caring for what we all consider to be the common understanding of what is the truth, and cultivating an environment where the new generation grows up to learn what the proper scientific conduct is, and that it is not acceptable to seriously deviate from it.

Unfortunately, I am seeing more and more PhD students who are less and less interested in correctness, precision, and what I’d consider proper scientific conduct. They have learned from those successful in the field what does and does not matter. I remember when someone once told me that my approach for a particular algorithm was wrong (about strongly connected components discovery), and how upset I was that I had no idea. I went home that day and I immediately fixed my tool to use the right approach (i.e. to use Tarjan’s algorithm). I felt deep shame that I had no clue what I was doing, and that I messed up. In contrast, recently talked with a PhD student who wrote a tool that was meant to perform well in certain contexts. When I explained the student that the approach to the evaluation was incorrect, they didn’t follow up at all. I think they were surprised that I later followed up, demonstrating that indeed the evaluation was not careful enough, and the tool is less useful than it seems from the paper’s evaluation. It was a strange experience — when I was a PhD student and someone sat down and showed me that what I was doing was potentially sloppy, I was super worried and very curious to find out what’s going on. I still remember this moment when a reviewer of my dissertation challenged a graph and I was stressed for a week before I figured out they misread the graph, because I didn’t label it clearly. Or when in a presentation I accidentally left out performing so-called ‘restarts’ as a major advancement in the history of SAT solvers, and someone in the audience rightfully pointed it out. I felt embarrassed for having made such a mistake.

I am not sure how to fix this problem. Seemingly, researchers are less and less keen on correctness, precision, and scientific curiosity. They don’t seem to be incentivised to do so. I sometimes wonder if the system has become so damaged, so many PhD students have grown up to be professors in this environment, that much of what I wrote here seems alien, even repulsive, to many. It makes me sad.

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