Imagine you’re looking for a good restaurant for a special dinner. You go on Google and see the usual stars, metrics, and ratings: “4.5 stars based on 120 reviews.” It looks good, but then you start to wonder… do those ratings really reflect the quality of the place? What if they’re from five years ago? Or if they all come from a very small group of people? Well, believe it or not, the exact same thing happens in science.
In the world of research, measuring the impact and “quality” of a paper or a scientist is no easy task. It’s like trying to evaluate that restaurant: it’s not enough to simply count how many citations a paper has (even though that’s useful); it also matters who is citing it, why they are doing so, and how long it remains relevant. This is where metrics like the famous H-index and the increasingly popular C-Score come into play.
H-index: the veteran of metrics
The H-index is probably the best-known indicator for measuring a researcher’s impact. Introduced in 2005 by physicist Jorge Hirsch, the H-index aims to strike a balance between quantity and quality. How does it work? Simple: a researcher has an H-index of, say, 10 if they have published 10 papers that have each been cited at least 10 times. Obviously, this index has a clear advantage: it is easy to understand and calculate. It also combines two key aspects: how many relevant papers you have and how much impact they’ve had (measured through citations). That’s why it has been so popular for years.
However, not everything is as straightforward as it seems, and despite its enormous popularity, the H-index has important limitations. For example:
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It does not distinguish between old and recent citations. It makes no difference whether a paper was relevant 20 years ago but is no longer cited; it still counts the same as a current one.
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It does not evaluate who is citing you. All citations carry the same weight, whether they come from a giant in your field or from a journal hardly anyone reads.
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It does not reflect the impact of highly specialized fields very well. If you work on a niche topic with fewer publications and citations available, you may be undervalued, even if your research is crucial to that field.
So, while the H-index is useful, many experts agree that it doesn’t tell the whole story. And this is where the C-Score comes in.
C-Score: a more comprehensive view of impact
The C-Score (or C-Index) is a more recent indicator, so it may not sound as familiar, but it aims to address the limitations of the H-index. What makes it different? In short, the C-Score doesn’t just count citations; it also analyzes the relevance of those citations over time and who is doing the citing. In fact, one of the major advantages of the C-Score is that it recognizes that science is not static. In other words, it takes into account that while some papers become irrelevant over time, others may remain influential or even gain importance years after being published. The C-Score adapts to this reality by giving more weight to recent citations and to those coming from influential authors.
Still confused? Here are the three key differences between the H-index and the C-Score.
Why is the C-Score gaining ground?
With all this in mind, it seems only natural that more and more experts are recommending the use of the C-Score over the H-index. In a scientific environment where current relevance and the quality of citations matter more than ever, the C-Score offers a much richer view of the impact of a researcher or a publication.
Think about this: what better reflects the impact of an article? A fixed number that doesn’t change over time, or an index that evolves and recognizes both the quality and the persistence of its influence? In this sense, the C-Score is like a tool for “continuous evaluation” that doesn’t remain anchored in the past. Moreover, the C-Score is also fairer in specific scientific contexts. For example, researchers working in emerging or interdisciplinary fields often benefit from its ability to value the quality of citations rather than being limited to sheer quantity.
That said, as with everything in life, which metric to use depends on the context. If you need a quick and simple metric for a general evaluation, the H-index can still be useful. But if you are looking for a more detailed analysis—especially in environments where quality and current relevance are crucial—the C-Score is clearly superior.
The key point is to understand that metrics are neither perfect nor universal. At the end of the day, evaluating scientific impact requires combining different tools and, above all, keeping sight of what really matters: the quality and relevance of scientific contributions.