sample: 661,643 papers published in 2001 from 251 subject categories, retrieved from Web of Science

novelty = difficulty-adjusted first-time combination of journals

for analysis, novelty was binned into three categories (no novelty, >= 1 novel combination, but overall score < 99th percentile for subject category, >= 99th percentile for subject category) due to a highly skewed distribution

properties of novelty score: as expected, highly skewed (~89% of papers do not make novel combinations, and ones that do only score modestly after difficulty adjustment)

results robust to categorical conception of novelty, as well as filtering of sample (see 5.7)

summary fig

novelty strongly implies cross-disciplinary, but not the complement; [[EVD - highly novel combinations of cited journals in a paper were almost always cross-disciplinary, but cross-disciplinary combinations were infrequently novel - @wangBiasNoveltyScience2017]]

novelty --> higher variance in impact: [[EVD - highly novel papers had higher variance in their citation outcomes over a 15-year window, biased towards the higher impact tail of the distribution - @wangBiasNoveltyScience2017]]

novelty --> big hit, esp. in long run, and outside home field: [[EVD - highly novel papers were more likely to be in the top 1% of citations in the long run, but not in the short run, and particularly in other fields - @wangBiasNoveltyScience2017]]

novelty --> lower impact factor journal, ceteris paribus: [[EVD - highly novel papers were more likely to be published in lower impact journals - @wangBiasNoveltyScience2017]]

main claim here is that [[CLM - bibliometric measures are biased against novel breakthrough research - @wangBiasNoveltyScience2017]]

idea of "sleeping beauty" seems interesting, also as a keyword related to [[CLM - True creative breakthroughs often take a long time to develop]]

authors propose novel conceptual combinations (implicitly "inside a discipline"?? or outside??) as mechanisms for outsider innovation

the measurement of impact in terms of the long run outcomes for a single paper seem... off to me. i want to know about field-wide changes. which i suppose is not really possible to glean from citation data, because they involve counterfactuals. maybe in some limited cases we can get some natural experiments to compare.

i keep thinking about relationships between this and literature-based discovery! which has a similar intuition about basically "undiscovered public knowledge" (undiscovered fruitful connections between things)

the results on delayed recognition and lower-impact factor REALLY throw a wrench in heuristics that focus on filtering papers based on "top-tier venues"

should really pay attention to the variance in these effects: how tight is the relationship? we know for sure that the variance in hits

some questioning of the novelty measure in a later paper (@bornmannWeMeasureNovelty2019) comparing it to @uzziAtypicalCombinationsScientific2013's measure in terms of correlation to F1000 Prime ratings of biomed papers: depending on how you slice it, this is a plus or minus, since one key result in Wang is the asymmetry between home vs. foreign impact, and these F1000 tags are for papers that get recommended

basic result of [[EVD - highly novel papers were more likely to be in the top 1% of citations in the long run, but not in the short run, and particularly in other fields - @wangBiasNoveltyScience2017]] seems to be replicated by a later shorter paper focusing only on physicists: @mairesseNoveltyScienceImpact2018 - noting that this was a sample of papers from 2005-2009, so it starts to get a little bit at my misgivings about generalizing from a sample from 2001 (although it also pushes in the opposite direction by focusing only on physics)