How many citations does a paper have to get before it’s significantly above baseline impact for the field?

[Note: This blog post was originally hidden because it’s not aimed at my usual audience. I decided to open it up because, hey, I guess it’s okay for all you humanists and data scientists to know that one of the other hats I wear is that of an informetrician. Another reason I kept it hidden is because I’m pretty scared of how people use citation impact ratings to evaluate research for things like funding and tenure, often at the expense of other methods that ought be used when human livelihoods are at stake. So please don’t do that.]

It depends on the field, and field is defined pretty loosely. This post is in response to a twitter conversation between mrgunn, myself, and some others. mrgunn thinks citation data ought to be freely available, and I agree with him, although I believe data is difficult enough to gather and maintain that a service charge for access is fair, if a clever free alternative is lacking. I’d love to make a clever free alternative (CiteSeerX already is getting there), but the best data still comes from expensive sources like ISI’s Web of Science or Scopus.

At any rate, the question is an empirical one, and one that lots of scientometricians have answered in a number of ways. I’m going to perform my own SSA (Super-Stupid Analysis) here, and I won’t bother taking statistical regression models or Bayesian inferences into account, because you can get a pretty good sense of “impact” (if you take citations to be a good proxy for impact, which is debatable – I won’t even get into using citations as a proxy for quality) using some fairly simple statistics. For the mathy and interested, a forthcoming paper by Evans, Hopkins, and Kaube treats the subject more seriously in Universality of Performance Indicators based on Citation and Reference Counts.

I decided to use the field of Scientometrics, because it’s fairly self-contained (and I love being meta), and I drew my data from ISI’s Web of Science. I retrieved all articles published in the journal Scientometrics up until 2009, which is a nicely representative sample of the field, and then counted the number of citations to each article in a given year. Keep in mind that if you’re wondering how much your Scientometrics paper stood out above its peers in citations with this chart, you have to use ISI’s citation count to your paper, otherwise you’re comparing apples to something else that isn’t apples.

Figure 1. Histogram of citations to papers, with the height of each bar representing the number of papers cited x times. The colors break down the bars by year. (Click to enlarge)
Figure 2. Same as Figure 1, but with the x axis on a log scale.

According to Figure 1 and Figure 2 (Fig. 2 is the same as Fig. 1 but with the x axis on a log scale to make the data a bit easier to read), it’s immediately clear that citations aren’t normally distributed. This tells us right away that some basic statistics simply won’t tell us much with regards to this data. For example, if we take the average number of citations per paper, by adding up each paper’s citation count and dividing it by the total number of papers, we get 7.8 citations per paper. However, because the data are so skewed to one side, over 70% of the papers in the set fall below that average (that is, 70% of papers are cited fewer than 7 times). In this case, a slightly better measurement would be the median, which is 4. That is, about half the papers have fewer than four citations. About a fifth of the papers have no citations at all.

If we look at the colors of Figure 1, which breaks down each bar by year, we can see that the data aren’t really evenly distributed by years, either. Figure 3 breaks this down a bit better.

Figure 3. Number of papers to articles in the journal Scientometrics, colored by number of citations each received.

In Figure 3, you can see the amount of papers published in a given year, and the colors represent how many citations each paper got that year, with the red end of the spectrum showing papers cited very little, and the violet end of the spectrum showing highly cited papers. Immediately we see that the most recent papers don’t have many highly cited articles, so the first thing we should do is normalize by year. That is, an article published this year shouldn’t be placed against the same standards as an article that’s had twenty years to slowly accrue citations.

To make these data a bit easier to deal with, I’ve sliced the set into 8-year chunks. There are smarter ways to do this, but like I said, we’re keeping the analysis simple for the sake of presentation. Figure 4 is the same as Figure 3, but separated out into the appropriate time slices.

Figure 4. Same as figure 3, but separated into 8 year time slices.

Now, to get back to the original question, mrgunn asked how many citations a paper needs to be above the fold. Intuitively, we’d probably call a paper highly impactful if it’s in the blue or violet sections of its time slice (sorry for those of you who are colorblind, I just mean the small top-most area). There’s another way to look at these data that’ll make it a bit easier to eyeball how much more citations a paper’s received than its peers; a density graph. Figure 5 shows just that.

Figure 5. Each color blob represents a time slice, with the height at any given point representing the proportion of papers in that chunk of time which have x citations. The x axis is on a log scale.

Looking at Figure 5, it’s easy to see that a paper published before 2008 with fewer than half a dozen citations is clearly below the norm. If the paper were published after 2008, it could be above the norm even if it had only a small handful of citations. A hundred citations is clearly “highly impactful” regardless of the year the paper was published. To get a better sense of papers that are above the baseline, we can take a look at the actual numbers.

The table below (excuse the crappy formatting, I’ve never tried to embed a big table in WP before) shows the percent of papers which have x citations or fewer in a given time slice. That is, 24% of papers published before 1984 have no citations to them, 31% of papers published before 1984 have 0 or 1 citations to them, 40% of papers published before 1984 have 0, 1, or 2 citations to them, and so forth. That means if you published a paper in Scientometrics  in 1999 and ISI’s Web of Science says you’ve received 15 citations, it means your paper has received more citations than 80% of the other papers published between 1992 and 2000.

[table id=2 /]

 

The conversation also brought up the point of whether this should be a clear binary at the ends of the spectrum (paper A is low impact because it received only a handful of citations, paper B is high impact because it received 150, but we can’t really tell anything in between), or whether we could get a more nuanced few of the spectrum. A combined qualitative/quantitative analysis would be required for a really good answer to that question, but looking at the numbers in the table above, we can see pretty quickly that while 1 citation is pretty different from 2 citations, 38 citations is pretty much the same as 39. That is, the “jitter” of precision probably increases exponentially the more citations you’ve received, such that with very few citations the “impact” precision is quite high, and that precision gets exponentially lower the more citations you’ve received.

All this being said, I do agree with mrgunn that a free and easy to use resource for this sort of analysis would be good. However, because citations often don’t equate to quality, I’d be afraid this tool would just make it easier and more likely for people to make sweeping and inaccurate quality measurements for the purpose of individual evaluations.

Science Systems Engineering

Warning: This post is potentially evil, and definitely normative. While I am unsure whether what I describe below should be doneI’m becoming increasingly certain that it could be. Read with caution.

Complex Adaptive Systems

Science is a complex adaptive system. It is a constantly evolving network of people and ideas and artifacts which interact with and feed back on each other to produce this amorphous socio-intellectual entity we call science. Science is also a bunch of nested complex adaptive systems, some overlapping, and is itself part of many other systems besides.

The study of complex interactions is enjoying a boom period due to the facilitating power of the “information age.” Because any complex system, whether it be a social group or a pool of chemicals, can exist in almost innumerable states while comprising the same constituent parts, it requires massive computational power to comprehend all the many states a system might find itself in. From the other side, it takes a massive amount of data observation and collection to figure out what states systems eventually do find themselves in, and that knowledge of how complex systems play out in the real world relies on collective and automated data gathering. From seeing how complex systems work in reality, we can infer properties of their underlying mechanisms; by modeling those mechanisms and computing the many possibilities they might allow, we can learn more about ourselves and our place in the larger multisystem. 1

One of the surprising results of complexity theory is that seemingly isolated changes can produce rippling, massive effects throughout a system.  Only a decade after the removal of big herbivores like giraffes and elephants from an African savanna, a generally positive relationship between bugs and plants turned into an antagonistic one. Because the herbivores no longer grazed on certain trees, those trees began producing less nectar and fewer thorns, which in turn caused cascading repercussions throughout the ecosystem. Ultimately, the trees’ mortality rate doubled, and a variety of species were worse-off than they had been. 2 Similarly, the introduction of an invasive species can cause untold damage to an ecosystem, as has become abundantly clear in Florida 3 and around the world (the extinction of flightless birds in New Zealand springs to mind).

http://www.flickr.com/photos/arnolouise/3202569865/

Both evolutionary and complexity theories show that self-organizing systems evolve in such a way that they are self-sustaining and self-perpetuating. Often, within a given context or environment, the systems which are most resistant to attack, or the most adaptable to change, are the most likely to persist and grow. Because the entire environment evolves concurrently, small changes in one subsystem tend to propagate as small changes in many others. However, when the constraints of the environment change rapidly (like with the introduction of an asteroid and a cloud of sun-cloaking dust), when a new and sufficiently foreign system is introduced (land predators to New Zealand), or when an important subsystem is changed or removed (the loss of megafauna in Africa), devastating changes ripple outward.

An environmental ecosystem is one in which many smaller overlapping systems exist, and changes in the parts may change the whole; society can be described similarly. Students of history know that the effects of one event (a sinking ship, an assassination, a terrorist attack) can propagate through society for years or centuries to come. However, a system not merely a slave to these single occurrences which cause Big Changes. The structure and history of a system implies certain stable, low energy states. We often anthropomorphize the tendency of systems to come to a stable mean, for example “nature abhors a vacuum.” This is just the manifestation of the second law of thermodynamics: entropy always increases, systems naturally tend toward low energy states.

For the systems of society, they are historically structured constrained in such a way that certain changes would require very little energy (an assassination leading to war in a world already on the brink), whereas others would require quite a great deal (say, an attempt to cause war between Canada and the U.S.). It is a combination of the current structural state of a system and the interactions of the constituent parts that lead that system in one direction or another. Put simply, a society, its people, and its environment are responsible for its future. Not terribly surprising, I know, but the formal framework of complexity theory is a useful one for what is described below.

metastability

The above picture, from the Wikipedia article on metastability, provides an example of what’s described above. The ball is resting in a valley, a low energy state, and a small change may temporarily excite the system, but the ball eventually finds its way into the same, or another, low energy state. When the environment is stable, its subsystems tend to find comfortably stable niches as well. Of course, I’m not sure anyone would call society wholly stable…

Science as a System

Science (by which I mean wissenschaft, any systematic research) is part of society, and itself includes many constituent and overlapping parts. I recently argued, not without precedent, that the correspondence network between early modern Europeans facilitated the rapid growth of knowledge we like to call the Scientific Revolution. Further, that network was an inevitable outcome of socio/political/technological factors, including shrinking transportation costs, increasing political unrest leading to scholarly displacement, and, very simply, an increased interest in communicating once communication proved so fruitful. The state of the system affected the parts, the parts in turn affected the system, and a growing feedback loop led to the co-causal development of a massive communication network and a period of massively fruitful scholarly work.

Scientific Correspondence Network

Today and in the past, science is embedded in, and occasionally embodied by, the various organizational and communicative hierarchies its practitioners find themselves in. The people, ideas, and products of science feed back on one another. Scientists are perhaps more affected by their labs, by the process of publication, by the realities of funding, than they might admit. In return, the knowledge and ideas produced by science, the message, shape and constrain the medium in which they are propagated. I’ve often heard and read two opposing views: that knowledge is True and Right  and unaffected the various social goings on of those who produce it, and that knowledge is Constructed and Meaningless outside of the social and linguistic system it resides in. The truth, I’m sure, is a complex tangle somewhere between the two, and affected by both.

In either case, science does not take place in a vacuum. We do our work through various media and with various funds, in departments and networks and (sometimes) lab-coats, using a slew of carefully designed tools and a language that was not, in general, made for this purpose. In short, we and our work exist in a  complex system.

Engineering the Academy

That system is changing. Michael Nielsen’s recent book 4 talks about the rise of citizen science, augmented intelligence, and collaborative systems as not merely as ways to do what we’ve already done faster, but as new methods of discovery. The ability to coordinate on such a scale, and in such new ways, changes the game of science. It changes the system.

While much of these changes are happening automatically, in a self-organized sort of way, Nielsen suggests that we can learn from our past and learn from other successful collective ventures in order to make a “design science of collaboration.” That is, using what we know of how people work together best, of what spurs on the most inspired research and the most interesting results, we can design systems to facilitate collaboration and scientific research. In Nielsen’s case, he’s talking mostly about computer systems; how can we design a website or an algorithm or a technological artifact that will aid in scientific discovery, using the massive distributed power of the information age? One way Nielson points out is “designed serendipity,” creating an environment where scientists are more likely experience serendipitous occurrences, and thus more likely to come up with innovated and unexpected ideas.

Can we engineer science? http://www.flickr.com/photos/seattlemunicipalarchives/4818952324

In complexity terms, this idea is restructuring the system in such a way that the constituent parts or subsystems will be or do “better,” however we feel like defining better in this situation. It’s definitely not the first time an idea like this has been used. For example, science policy makers, government agencies, and funding bodies have long known that science will often go where the money is. If there is a lot of money available to research some particular problem, then that problem will tend to get researched. If the main funding requires research funded to become open access, by and large that will happen (NIH’s PubMed requirements).

There are innumerable ways to affect the system in a top-down way in order to shape its future. Terrence Deacon writes about how it is the constraints on a system which tend it toward some equilibrium state 5; by shaping the structure of the scientific system, we can predictably shape its direction. That is, we can artificially create a low energy state (say, open access due to policy and funding changes), and let the constituent parts find their way into that low energy state eventually, reaching equilibrium. I talked a bit more about this idea of constraints leading a system in a recent post.

As may be recalled from the discussion above, however, this is not the only way to affect a complex system. External structural changes are only part of the story of how a system grows shifts, but only a small part of the story. Because of the series of interconnected feedback loops that embody a system’s complexity, small changes can (and often do) propagate up and change the system as a whole. Lie, Slotine, and Barabási recently began writing about the “controllability of complex networks 6,”  suggesting ways in which changing or controlling constituent parts of a complex system can reliably and predictably change the entire system, perhaps leading it toward a new preferred low energy state. In this case, they were talking about the importance of well-connected hubs in a network; adding or removing them in certain areas can deeply affect the evolution of that network, no matter the constraints. Watts recounts a great example of how a small power outage rippled into a national disaster because just the right connections were overloaded and removed 7. The strategic introduction or removal of certain specific links in the scientific system may go far toward changing the system itself.

Not only is science is a complex adaptive system, it is a system which is becoming increasingly well-understood. A century of various science studies combined with the recent appearance of giant swaths of data about science and scientists themselves is beginning to allow us to learn the structure and mechanisms of the scientific system. We do not, and will never, know the most intricate details of that system, however in many cases and for many changes, we only need to know general properties of a system in order to change it in predictable ways. If society feels a certain state of science is better than others, either for the purpose of improved productivity or simply more control, we are beginning to see which levers we need to pull in order to enact those changes.

This is dangerous. We may be able to predict first order changes, but as they feed back onto second order, third order, and further-down-the-line changes, the system becomes more unpredictable. Changing one thing positively may affect other aspects in massively negative (and massively unpredictable) ways.

However, generally if humans can do something, we will. I predict the coming years will bring a more formal Science Systems Engineering, a specialty apart from science policy which will attempt to engineer the direction of scientific research from whatever angle possible. My first post on this blog concerned a concept I dubbed scientonomy, which was just yet another attempt at unifying everybody who studies science in a meta sort of way. In that vocabulary, then, this science systems engineering would be an applied scientonomy. We have countless experts in all aspects of how science works on a day-to-day basis from every angle; that expertise may soon become much more prominent in application.

It is my hope and belief that a more formalized way of discussing and engineering scientific endeavors, either on the large scale or the small, can lead to benefits to humankind in the long run. I share the optimism of Michael Nielsen in thinking that we can design ways to help the academy run more smoothly and to lead it toward a more thorough, nuanced, and interesting understanding of whatever it is being studied. However, I’m also aware of the dangers of this sort of approach, first and foremost being disagreement on what is “better” for science or society.

At this point, I’m just putting this idea out there to hear the thoughts of my readers. In my meatspace day-to-day interactions, I tend to be around experimental scientists and quantitative social scientists who in general love the above ideas,  but at my heart and on my blog I feel like a humanist, and these ideas worry me for all the obvious reasons (and even some of the more obscure ones). I’d love to get some input, especially from those who are terrified that somebody could even think this is possible.

Notes:

  1. I’m coining the term “multisystem” because ecosystem is insufficient, and I don’t know something better. By multisystem, I mean any system of systems; specifically here, the universe and how it evolves. If you’ve got a better term that invokes that concept, I’m all for using it. Cosmos comes to mind, but it no longer represents “order,” a series of interlocking systems, in the way it once did.
  2. Palmer, Todd M, Maureen L Stanton, Truman P Young, Jacob R Goheen, Robert M Pringle, and Richard Karban. 2008. “Breakdown of an Ant-Plant Mutualism Follows the Loss of Large Herbivores from an African Savanna.” Science319 (5860) (January 11): 192–195. doi:10.1126/science.1151579.
  3. Gordon, Doria R. 1998. “Effects of Invasive, Non-Indigenous Plant Species on Ecosystem Processes: Lessons From Florida.” Ecological Applications 8 (4): 975–989. doi:10.1890/1051-0761(1998)008[0975:EOINIP]2.0.CO;2.
  4. Nielsen, Michael. Reinventing Discovery: The New Era of Networked Science. Princeton University Press, 2011.
  5. Deacon, Terrence W. “Emergence: The Hole at the Wheel’s Hub.” In The Re-Emergence of Emergence: The Emergentist Hypothesis from Science to Religion, edited by Philip Clayton and Paul Davies. Oxford University Press, USA, 2006.
  6. Liu, Yang-Yu, Jean-Jacques Slotine, and Albert-László Barabási. “Controllability of Complex Networks.” Nature473, no. 7346 (May 12, 2011): 167–173.
  7. Watts, Duncan J. Six Degrees: The Science of a Connected Age. 1st ed. W. W. Norton & Company, 2003.

The Networked Structure of Scientific Growth

Well, it looks like Digital Humanities Now scooped me on posting my own article. As some of you may have read, I recently did not submit a paper on the Republic of Letters, opting instead to hold off until I could submit it to a journal which allowed authorial preprint distribution. Preprints are a vital part of rapid knowledge exchange in our ever-quickening world, and while some disciplines have embraced the preprint culture, many others have yet to. I’d love the humanities to embrace that practice, and in the spirit of being the change you want to see in the world, I’ve decided to post a preprint of my Republic of Letters paper, which I will be submitting to another journal in the near future. You can read the full first draft here.

The paper, briefly, is an attempt to contextualize the Republic of Letters and the Scientific Revolution using modern computational methodologies. It draws from secondary sources on the Republic of Letters itself, especially from my old mentor R.A. Hatch, some network analysis from sociology and statistical physics, modeling, human dynamics, and complexity theory. All of this is combined through datasets graciously donated by the Dutch Circulation of Knowledge group and Oxford’s Cultures of Knowledge project, totaling about 100,000 letters worth of metadata. Because it favors large scale quantitative analysis over an equally important close and qualitative analysis, the paper is a contribution to historiopgraphic methodology rather than historical narrative; that is, it doesn’t say anything particularly novel about history, but it does offer a (fairly) new way of looking at and contextualizing it.

A visualization of the Dutch Republic of Letters using Sci2 & Gephi

At its core, the paper suggests that by looking at how scholarly networks naturally grow and connect, we as historians can have new ways to tease out what was contingent upon the period and situation. It turns out that social networks of a certain topology are basins of attraction similar to those I discussed in Flow and Empty Space. With enough time and any of a variety of facilitating social conditions and technologies, a network similar in shape and influence to the Republic of Letters will almost inevitably form. Armed with this knowledge, we as historians can move back to the microhistories and individuated primary materials to find exactly what those facilitating factors were, who played the key roles in the network, how the network may differ from what was expected, and so forth. Essentially, this method is one base map we can use to navigate and situate historical narrative.

Of course, I make no claims of this being the right way to look at history, or the only quantitative base map we can use. The important point is that it raises new kinds of questions and is one mechanism to facilitate the re-integration of the individual and the longue durée, the close and the distant reading.

The project casts a necessarily wide net. I do not yet, and probably could not ever, have mastery over each and every disciplinary pool I draw from. With that in mind, I welcome comments, suggestions, and criticisms from historians, network analysts, modelers, sociologists, and whomever else cares to weigh in. Whomever helps will get a gracious acknowledgement in the final version, good scholarly karma, and a cookie if we ever meet in person. The draft will be edited and submitted in the coming months, and if you have ideas, please post them in the comment section below. Also, if you use ideas from the paper, please cite it as an unpublished manuscript or, if it gets published, cite that version instead.

Pledges

I know I’m a little late to the game, but open access is important year-round, and I only just recently got the chance to write these up. Below are my pledges to open access, which can also be found on the navigation tab above.

The system of pay-to-subscribe journals that spent so many centuries helping the scholarly landscape coordinate and collaborate is now obsolete; a vestigial organ in the body of science.

These days, most universities offer free web access and web hosting. These two elements are necessary, though not sufficient, for a free knowledge economy. We also need peer review (or some other, better form of quality control), improved reputation management (citations++), and some assurance that data/information will last. These come at a cost, but those costs can be paid by the entire scholarly market, and the fruits enjoyed within and without.

If you think open access is important, you should also consider pledging to support open access. Publishing companies have a lot of money invested in keeping things as they are, and only a concerted effort on behalf of the scholars feeding and using the system will be able to change it.

Scholarship is no longer local, and it’s about time our distribution system followed suit.

—-

I pledge to be a good scholarly citizen. This includes:

  • Opening all data generated by me for the purpose of a publication at the time of publication. 1
  • Opening all code generated by me for the purpose of a publication at the time of publication.
  • Freely distributing all published material for which I have the right, and fighting to retain those rights in situations where that is not the case.
  • Fighting for open access of all materials worked on as a co-author, participant in a grant, or consultant on a project.
I pledge to support open access by:
  • Only reviewing for journals which plan to release their publications openly.
  • Donating to free open source software initiatives where I would otherwise have paid for proprietary software.
  • Citing open publications if there is a choice between two otherwise equivalent sources.
I pledge never to let work get in the way of play.
I pledge to give people chocolate occasionally if I think they’re awesome.
_

Notes:

  1. unless there are human subjects and privacy concerns

Psychology of Science as a New Subdiscipline in Psychology

Feist, G. J. 2011. “Psychology of Science as a New Subdiscipline in Psychology.” Current Directions in Psychological Science 20 (October 5): 330-334. doi:10.1177/0963721411418471.

Gregory Feist, a psychologist from San Jose State University, recently wrote a review of the past decade of findings in the psychology of science. He sets the discipline apart from history, philosophy, anthropology, and sociology of science, defining the psychology of science as “the scientific study of scientific thought and behavior,” both implicit and explicit, in children and adults.

Some interesting results covered in the paper:

  • “People pay more attention to evidence when it concerns plausible theories than when it concerns implausible ones.”
  • “Babies as young as 8 months of age understand probability… children as young as 4 years old can correctly draw causal inferences from bar graphs.” (I’m not sure how much I believe that last one – can grown scientists correctly draw causal inferences from bar graphs?)
  • “children, adolescents, and nonscientist adults use different criteria when evaluating explanations and evidence, they are not very good at separating belief from fact (theory and evidence), and they persistently give their beliefs as evidence for their beliefs.”
  • “one reason for the inability to distinguish theory from evidence is the belief that knowledge is certain and absolute—that is, either right or wrong”
  • “scientists use anomalies and unexpected findings as sources for new theories and experiments and that analogy is very important in generating hypotheses and interpreting results”
  • “the personality traits that make scientific interest more likely are high conscientiousness and low openness, whereas the traits that make scientific creativity more likely are high openness, low conscientiousness, and high confidence.”
  • “scientists are less prone to mental health difficulties than are other creative people,” although “It may be that science tends to weed out those with mental health problems in a way that art, music, and poetry do not.”
It is somewhat surprising that Feist doesn’t mention the old use of “psychology of science,” which largely surrounded Reichenbach’s (1938) context distinctions, as echoed by the Vienna Circle and many others. The context of discovery (rather than the context of justification) deals with the question that, as Salmon (1963) put it, “When a statement has been made, … how did it come to be thought of?” Barry F. Singer (1971) wrote “Toward a Psychology of Science,” where he quoted S.S. Stevens (1936, 1939) on the subject of a scientific psychology of science.
It is exciting that the psychology of science is picking up again as an interesting object of study, although it would have been nice for Feist to cite someone earlier than 1996 when discussing this “new subdiscipline in psychology.”
From Wired Magazine

Scientonomy

or Yet Another New Name.

Scientonomy. n.
1. The scientific study of science and scientists, especially their interactions, creative activities, and specific objects of research.
2. A system of knowledge or beliefs about science, broadly construed.

I hope science to be taken in its broader sense, like the German’s wissenschaft, described by Wikipedia as “any study or science that involves systematic research and teaching.” This extends scientonomy to the study of most subjects taught in academia, and many that exist well outside of it. Also, it’s worth noting that “the scientific study of…” should also be taken as wissenschaft; that is, using more than just natural science methodologies to study science. This includes methods from the humanities.

Science comes from a Latin word meaning to know,” and it is knowledge and its creation and assorted practices I wish to explore. The suffix -nomy is ancient Greek, meaning law, custom, arrangement, or system of rules. They come from two different languages; deal with it. I would use episteme rather than scientia, however its connotations are too loaded, and it is too separate from its brother techne, to be useful for my purposes.

It is important that I use the root science, as this project does not seek to understand knowledge in a vacuum, or various possibilities of how knowledge and knowledge creation may work, but rather how  humanity has actually practiced scientific creation and distribution, and the associations and repercussions those practices have had (and gleaned from) the world at large.

The suffix -onomy is the natural choice for two reasons. First, scientonomy could be an unobtrusive measurement in the same way astronomy is. That is, the act of collecting and analyzing scientonomic data in a way that does not intrude on the science and scientists themselves, from a distance and using their traces, much like the way astronomers view their subjects from a distance without direct experimentation. This in no way means scientonomy would make no mark on science; indeed, much like astronomy helped pave the way for the space program and allowed us to put footprints on the moon, scientonomy has the power to greatly affect the objects of its study.

Boyack, Klavans, and others

Like scientometrics, from which springs the dreaded h-index and other terrifying ways of measuring scientific output, scientonomy wields a dangerous weapon: the power to positively or negatively affect the scientific process. Scientonomy should be cautious, but not lame; we should work to improve the rate and process of scientific discovery and dissemination, we just need to be extremely careful about it.

The second reason for –onomy is a bit sillier, and possibly somewhat self-serving. All the other good names were taken, and already mean slightly different things. We already have Science of Science (Burnet, 1774; Fichte, 1808; Ossowska & Ossowski 1935; Goldsmith 1966) which is actually pretty close to what I’m doing, but not a terribly catchy name; Scientometrics (Price, 1963) which focuses a bit too much on communicative traces at the expense of, say, philosophical accounts; Scientosophy (Goldsmith 1966; Konner, 2007) which sounds too much like science as philosophy; Scientography (Goldsmith, 1966; Vladutz, before 1977; Garfield, 1986) which deals mostly with maps; Scientopograhy (Schubert & Braun, 1996) which focuses on geographic/scientific relations; as well as meta-scientific catch-alls like STS, HPS, Sociology of Science, etc. which all have their own associated practices, all of which have a place in scientonomy. There’s also Scientology, which I won’t even bother getting into here, and (hopefully) has no place in scientonomy.