Editors and publishers often feel ashamed when they find themselves beholden to rough measures like the Impact Factor. In the cool-weather months of the year, they can even become defiant — gathered in enclaves and protected from winter’s chill, they gird their courage with drink and boasts of indifference to the measure. But, as winter thaws into summer and a fresh set of numbers blooms, these defiant feelings melt like poolside popsicles, with many of these same rebels turning into puddles of fawning, adoring Impact Factor fans.
I can’t count how many times I’ve heard editors and publishers proclaim that they will no longer be held rapt by silly metrics like the Impact Factor, Eigenfactor, h-index, or immediacy index — only to see them minutes or hours later unselfconsciously strategizing how to improve their Impact Factors by attracting better papers, working more closely with authors, attending more meetings, or eliminating editorial features that weigh down the overall calculation.
This occurred once again this year, but in duplicate, as the Impact Factors for 2013 were delayed by over a month, allowing everyone time to get keyed up, reset, and then get keyed up again.
Regret comes later, when temperatures fall, to be followed again by renewed feelings of defiance.
Robust online analytics only exacerbate these feelings, as now editors and publishers possess new metrics and measurements through which they can view editorial success or failure. In fields that naturally reward deconstruction and pushing to the elemental, the traditional rough approximations are somewhat counter-culture, and strike some as inadequate. Detailed online metrics reinforce this perception.
In contrast to new mounds of specific data, the Impact Factor can seem crude — it’s not nearly as detailed, its time-lag is significant, and it’s unclear how applicable it is for any particular author. Attempts to address these apparent inadequacies have led to a number of altmetrics, which oddly seem to always point back to achieving an impact factor proxy. This has become a source of some shame in the altmetrics community. Article-level metrics are another attempt to improve the situation.
But should anybody feel ashamed about relying on rough measures of value and prestige when it comes to scientific and scholarly journals? Perhaps these aggregate metrics are the best we’ll ever have or ever need.
Journals are inherently unpredictable, which may make rough approximations exactly the measures that matter. In this light, the Impact Factor is an approximate metric of value and importance, just as brand and editorial board composition can be.
These approximations may directly reflect the fact that journals are abstract approximations themselves.
There are many things to approximate when setting up a journal — frequency, scope, aim, editorial tone. And these approximations can change as publishers, editors, and audiences shift around. However good the approximation is at the outset, each journal then becomes subject to other approximations — those of authors. They will decide where the journal is in their submission pecking order, whether its aims and scope fit their research output, and so forth. Those who see a reason to submit will do so. Those who do not, will not.
This creates the unpredictable flow of manuscripts, a feature of journal publishing that never goes away or becomes very controllable. Editors can try to influence authors, appeal to them at meetings, make personal contact, or call in favors. But these techniques only go so far. Ultimately, authors provide a second-layer approximation of what a journal will be. They vote with their submissions.
Because journals rely on an uncertain flow of submissions — the volume of submissions is relatively unpredictable, the type of submissions is also unpredictable — journal issues aren’t comparable. I can recall years in which it seemed that all the best papers were negative trials. They were published, but it changed the journal significantly for a year. In other years, all the best papers come from one specific sub-discipline, reflecting either blind chance or funding decisions made years earlier. The fact is that any issue of any journal is unique, consisting of papers the editors cannot recruit again. Blending topics can conceal this unpredictability to some extent, but not entirely.
In essence, every issue of a journal is a rough approximation of a journal concept.
The word “granular” has some relevance here, as we often say, in our modern trendy talk, that we want more granular data, more granular measures, and more granular insights. But it’s easy to lose the forest for the trees — or, to stick with the “granular” theme, it’s easy to lose the beach for the grains of sand. Is that a nice sunny spot above the rising tide? Let’s toss the blanket there. Do I need to understand that there is a higher concentration of calcium in this section of the beach? Do I need to measure the average micron size of the grains here compared to 30 feet down the beach? Or would that be unreasonable?
With online data, we can now measure article performance with a much higher degree of granularity. But once measured, where does that leave the editor and publisher? Usually, nowhere.
Let’s assume that Article A has an incredible number of downloads and social media interactions. Article A is on Topic A. Logically, I’d want Article B on Topic A, to follow up on this success. There are three immediate concerns. First, Topic A is only one of 15 topics this journal needs to cover. Second, there is no good Article B on Topic A in the hopper. Third, by the time Article B on Topic A arrives, is reviewed, edited, and published, Topic A might have become much less interesting, for a variety of reasons.
But what about authors? Shouldn’t they have more granular data? This is where incentives come in. As noted above, most Impact Factors are heavily skewed by a few highly cited papers. For these authors, more granular data would be beneficial. But for the rest of the authors in any fetch of Impact Factor data, more granular data would uncover that their citation rate is below — and sometimes far below — the presumptive rate of any Impact Factor score. As for downloads and views, again, there is risk. I’ve had authors react in many different ways based on their expectations and the actual data. Some are pleased. Others are disappointed. In some cases, articles have received zero views. Zero. In these cases, authors are sorry they asked.
Even with these precise data, where does an author stand? Can there be any predictability based on one paper in a single journal? Some of the authors who have experienced low usage in some cases have had outstanding citation and traffic results in other cases. Not all papers are created equal, and not all topics resonate. Granularity presumes predictability — after all, knowing detailed data suggests you want to manage something next time. But what is there to manage?
We often say we “measure to manage.” But journals aren’t manageable in this way. Making the scientific and scholarly journals marketplace a precise and predictable market would mean adding incredible constraints on authors and editors. Getting granular data and using it meaningfully would trap journals in a mold that is both accidental and limiting. This is the major problem with analytics in scholarly publishing — there’s no recreating any issue or article we ever publish. They are all one-offs. They are each unique.
This ultimately gets to the value of journals as news sources in their respective fields. Echo chambers are easier to manage in a data-driven publishing model, but echo chambers aren’t news sources. News is messy. News is unpredictable. News is, well . . . news. And for this reason, measuring journal articles at a granular level is likely beyond the point of diminishing returns for anything other than directional editorial purposes — commissioning a review article, writing an editorial, and so forth. It also courts many risks that rough measures avoid — for authors, for editors, and for publishers.
Getting more granular than brand, editorial board, or Impact Factor is a fool’s errand, I believe, because the inflow of manuscripts is inherently unpredictable, as is the pool of authors and the range of topics. No editor or publisher can accurately promise a level of citation for any particular paper. This is evident in the skewness of Impact Factors, which are typically driven by a few high-citation papers and a longer tail of less-cited works. However, to predict or promise which papers will end up in the former category is a fraught endeavor at best.
So, while bowing before the approximations of the journals landscape may seem irrational to scientists, it is ultimately a necessary state in a free intellectual marketplace full of unpredictability. We are all approximating. There is no path to useful precision when predictability is not possible and not recommended.