論文引用的秘密，被揭露了

A new study has revealed an unsettling truth about the citation metrics that are commonly used to gauge scientists' level of impact and influence in their respective fields of research.

一項(xiàng)新的研究揭示了一個(gè)令人不安的事實(shí)，引用指標(biāo)通常被用來(lái)衡量科學(xué)家在各自研究領(lǐng)域的影響力。

Citation metrics indicate how often a scientist's research output is formally referenced by colleagues in the footnotes of their own papers – but a comprehensive analysis of this web of linkage shows the system is compromised by a hidden pattern of behaviour that often goes unnoticed.

引用指標(biāo)表明科學(xué)家的研究成果，在他們自己論文的腳注中被同行正式引用的頻率——但是對(duì)這一聯(lián)系網(wǎng)的全面分析表明，系統(tǒng)受到了一種經(jīng)常被忽視的隱藏行為模式的影響。

Specifically, among the 100,000 most cited scientists between 1996 to 2017, there's a stealthy pocket of researchers who represent "extreme self-citations and 'citation farms' (relatively small clusters of authors massively citing each other's papers)," explain the authors of the new study, led by physician turned meta-researcher John Ioannidis from Stanford University.

具體地說(shuō)，在1996年至2017年間被引用最多的10萬(wàn)名科學(xué)家中，有一個(gè)秘密的研究者口袋，他們代表著“極端的自我引用和‘引用農(nóng)場(chǎng)’(相對(duì)較小的一組作者大量引用彼此的論文)，”這項(xiàng)新研究的作者解釋說(shuō)，該研究由從斯坦福大學(xué)的內(nèi)科醫(yī)生轉(zhuǎn)為元研究員約翰·伊奧尼迪斯。

Ioannidis helps to run Stanford's meta-research innovation centre, called Metrics, which looks at identifying and solving systemic problems in scientific research.

約阿尼迪斯幫助運(yùn)營(yíng)斯坦福大學(xué)的元研究創(chuàng)新中心(Metrics)，該中心致力于識(shí)別和解決科學(xué)研究中的系統(tǒng)性問(wèn)題。

One of those problems, Ioannidis says, is how self-citations compromise the reliability of citation metrics as a whole, especially at the hands of extreme self-citers and their associated clusters.

伊安尼迪斯說(shuō)，其中一個(gè)問(wèn)題，是自我引文是如何損害引文指標(biāo)的可靠性的，特別是在極端自我引文者及其相關(guān)集群的手中。

"I think that self-citation farms are far more common than we believe," Ioannidis told Nature. "Those with greater than 25 percent self-citation are not necessarily engaging in unethical behaviour, but closer scrutiny may be needed."

“我認(rèn)為自我引用農(nóng)場(chǎng)比我們想象的要普遍得多，”約阿尼迪斯告訴《自然》雜志。“那些自我引證率超過(guò)25%的人不一定從事不道德的行為，但可能需要更仔細(xì)的審查。”
The 25 percent figure that Ioannidis is referring to are those scientists who self-refer 25 percent of the citations that reference their work (or that of their co-authors).

約阿尼迪斯所指的25%的數(shù)字是指那些在引用他們的著作(或他們的合著者的著作)的引文中，有25%是自我引用的科學(xué)家。

Being one-quarter of your own fan base might seem like a lot of self-citing, but it's not even that uncommon, the study reveals.

研究顯示，擁有四分之一的粉絲群似乎是一種自我引證，但這并不罕見(jiàn)。

Among the 100,000 most highly cited scientists for the period of 1996 to 2017, over 1,000 researchers self-cited more than 40 percent of their total citations – and over 8,500 researchers had greater than 25 percent self-citations.

在1996年至2017年10萬(wàn)名被引頻次最高的科學(xué)家中，超過(guò)1000名研究人員的自我引頻次占總引頻次的40%以上，超過(guò)8500名研究人員的自我引頻次超過(guò)25%。

There's no suggestion that any of these self-citations are necessarily or automatically unethical or unwarranted or self-serving in themselves. After all, in some cases, your own published scientific research may be the best and most relevant source to link to.

沒(méi)有任何跡象表明，這些自我引用行為中的任何一種是出自不道德的、無(wú)根據(jù)的或自私自利的。畢竟，在某些情況下，你自己發(fā)表的科學(xué)研究可能是最好和最相關(guān)的鏈接來(lái)源。

But the researchers behind the study nonetheless suggest that the prevalence of extreme cases revealed in their analysis debases the value of citation metrics as a whole – which are often used as a proxy of a scientist's standing and output quality (not to mention employability).

盡管如此，這項(xiàng)研究背后的研究人員表示，在他們的分析中揭示的極端案例的普遍存在，降低了引文指標(biāo)作為一個(gè)整體的價(jià)值——這些指標(biāo)通常被用作科學(xué)家地位和產(chǎn)出質(zhì)量的代表(更不用說(shuō)就業(yè)能力了)。

"With very high proportions of self-citations, we would advise against using any citation metrics since extreme rates of self-citation may herald also other spurious features," the authors write.

作者寫(xiě)道：“由于自我引用的比例非常高，我們建議不要使用任何引用指標(biāo)，因?yàn)闃O端的自我引用率可能預(yù)示著其他虛假特征。”

"These need to be examined on a case-by-case basis for each author, and simply removing the self-citations may not suffice."

“這些需要根據(jù)每個(gè)作者的具體情況進(jìn)行檢查，僅僅刪除自我引用可能還不夠。”

It's far from the first time researchers have highlighted serious problems with the way we rate the products of scientific endeavour.

這已經(jīng)不是研究人員第一次強(qiáng)調(diào)我們對(duì)科學(xué)成果的評(píng)估方式存在嚴(yán)重問(wèn)題。

In recent years, scientists have identified technical flaws hidden within citation systems, revealed shortcomings in how we rank science journals, and uncovered serious concerns about citation solicitations.

近年來(lái)，科學(xué)家們發(fā)現(xiàn)了引文系統(tǒng)中隱藏的技術(shù)缺陷，揭示了我們?nèi)绾螌?duì)科學(xué)期刊進(jìn)行排名的缺點(diǎn)，并發(fā)現(xiàn)了對(duì)引文征集的嚴(yán)重?fù)?dān)憂。

Others have noticed bizarre citation glitches that shouldn't exist at all, and observed other unsettling systemic trends that cast a shadow over a citation's worth.

其他人注意到一些本不應(yīng)該存在的奇怪的引文錯(cuò)誤，并觀察到其他令人不安的系統(tǒng)性趨勢(shì)，這些趨勢(shì)給引文的價(jià)值蒙上了陰影。
Amidst this mess, Ioannidis and his team hope their new data "will help achieve a more nuanced use of metrics" that enables the community as a whole to more easily identify and curtail the improper impact of self-citations and citation farms.

在這種混亂中，伊奧尼迪斯和他的團(tuán)隊(duì)希望他們的新數(shù)據(jù)“將有助于更細(xì)微地使用度量標(biāo)準(zhǔn)”，使整個(gè)社區(qū)能夠更容易地識(shí)別和減少自我引文和引文農(nóng)場(chǎng)的不當(dāng)影響。

Others, meanwhile, suggest the way to fix this is to get away from quantitative metrics as a whole, and focus instead on a qualitative approach to righting what's wrong here.

與此同時(shí)，另一些人則建議，解決這個(gè)問(wèn)題的方法是從整體上擺脫定量度量，而是專(zhuān)注于用定性的方法來(lái)糾正這里的錯(cuò)誤。

"When we link professional advancement and pay attention too strongly to citation-based metrics, we incentivise self-citation," psychologist Sanjay Srivastava from the University of Oregon, who wasn't involved in the study, told Nature.

俄勒岡大學(xué)的心理學(xué)家桑杰·斯里瓦斯塔瓦(Sanjay Srivastava)對(duì)《自然》雜志表示:“當(dāng)我們把職業(yè)發(fā)展和過(guò)于關(guān)注基于引用的指標(biāo)聯(lián)系起來(lái)時(shí)，我們會(huì)鼓勵(lì)自我引用。”他沒(méi)有參與這項(xiàng)研究。

"Ultimately, the solution needs to be to realign professional evaluation with expert peer judgement, not to double down on metrics."

“最終，解決方案需要重新調(diào)整專(zhuān)業(yè)評(píng)估與專(zhuān)家同行判斷，而不是在指標(biāo)上加倍。”

The findings are reported in PLOS Biology.

研究結(jié)果發(fā)表在《公共科學(xué)圖書(shū)館·生物學(xué)》雜志上。