Introduction

Research evaluation systems often seek simplicity. In environments characterized by complexity, diversity, and scale, there is a strong institutional preference for indicators that can summarize performance in a single value. Composite scores—numerical outputs derived from aggregating multiple indicators—have therefore become central to many evaluation frameworks.

These scores promise clarity. They allow institutions to rank researchers, compare universities, and communicate performance through a single, interpretable number.

Yet this simplicity is misleading.

Composite scores do not merely summarize research performance—they transform it. In the process of aggregation, they obscure the very dimensions they claim to represent. What appears as a coherent numerical evaluation is often the result of multiple hidden assumptions, methodological compromises, and arbitrary weighting decisions.

This editorial argues that composite scores fail not because they are technically flawed, but because they are structurally inadequate for representing the multidimensional nature of research.

1. The Appeal of Composite Scores

The widespread use of composite metrics is not accidental. Their appeal is rooted in several practical and institutional needs.

First, composite scores offer comparability. They enable ranking systems that position entities along a single scale, facilitating benchmarking across institutions and individuals.

Second, they provide communicability. A single score is easier to present, interpret, and disseminate than a set of disaggregated indicators.

Third, they support decision-making efficiency. Policymakers and administrators often require simplified representations to guide funding, hiring, and strategic planning.

These advantages explain why composite scores have become dominant in research evaluation. However, their utility comes at a methodological cost.

2. What Aggregation Conceals

Aggregation is not a neutral process. When multiple indicators are combined into a single score, several transformations occur simultaneously.

Different dimensions of research—such as productivity, citation impact, collaboration, and disciplinary variation—are compressed into a unified scale. This compression eliminates the ability to distinguish between distinct performance profiles.

For example, two researchers may receive identical composite scores while exhibiting entirely different strengths: one may excel in high-impact publications, while another demonstrates extensive collaboration and interdisciplinary engagement. The aggregated score masks these differences.

In this sense, composite metrics do not simply summarize—they erase structure.

3. The Problem of Weighting

At the core of every composite score lies a set of weighting decisions. These weights determine how different indicators contribute to the final score.

In many evaluation systems, weighting schemes are either insufficiently documented or presented without clear justification. Even when disclosed, they often reflect normative assumptions about what constitutes “valuable” research performance.

Weighting introduces several challenges:

it embeds subjective priorities within seemingly objective metrics

it creates trade-offs between indicators that may not be conceptually comparable

it amplifies certain dimensions while diminishing others

These decisions are rarely neutral. They shape evaluation outcomes in ways that are not always visible to users.

4. The Illusion of Precision

Composite scores often convey a sense of precision that exceeds their methodological foundations. Numerical outputs—especially when expressed with decimal accuracy—suggest a level of exactness that may not be warranted.

However, the underlying indicators may themselves be subject to:

incomplete data coverage

disciplinary biases

temporal limitations

methodological variability

When such indicators are aggregated, their uncertainties do not disappear. Instead, they are absorbed into a single number that appears stable and definitive.

This creates an illusion of precision: a perception that evaluation outcomes are more exact and reliable than the underlying data supports.

5. Comparability Without Equivalence

One of the primary justifications for composite scores is their ability to enable comparison. Yet comparison requires more than numerical alignment—it requires methodological equivalence.

When composite scores are used to compare entities across different disciplines, institutional contexts, or research systems, they risk conflating fundamentally different conditions. Aggregation does not resolve these differences; it conceals them.

As a result, composite metrics often produce rankings that appear coherent but lack interpretive validity.

Comparability, in this context, becomes a constructed outcome rather than a justified analytical condition.

6. From Scores to Profiles

If composite scores fail to represent the multidimensional nature of research, alternative approaches are required.

One such approach is the use of evaluation profiles—structured representations that preserve multiple indicators without collapsing them into a single value. Profiles allow for the simultaneous interpretation of different dimensions of research activity.

Rather than asking, “What is the overall score?” profiles ask:

What are the strengths and limitations across different dimensions?

How do indicators relate to each other?

What patterns emerge when metrics are interpreted collectively?

This shift transforms evaluation from numerical ranking to analytical interpretation.

7. Implications for Evaluation Systems

Moving beyond composite scores requires rethinking how evaluation systems are designed and used.

First, systems should prioritize disaggregated indicators over aggregated outputs. This preserves the informational structure necessary for interpretation.

Second, evaluation frameworks must provide transparent documentation of how indicators are constructed, normalized, and contextualized.

Third, platforms should discourage the use of single-score rankings as primary evaluation tools, particularly in high-stakes decision-making contexts.

Finally, users of evaluation systems must recognize that numerical simplicity does not equate to analytical validity.

Conclusion

Composite scores have become a dominant feature of research evaluation because they offer simplicity, comparability, and communicability. However, these advantages come at the expense of interpretive depth and methodological transparency.

By aggregating multiple indicators into a single value, composite metrics obscure the structural complexity of research performance. They embed subjective assumptions within numerical outputs, create illusions of precision, and enable comparisons that may lack validity.

The challenge for contemporary research evaluation is not to refine composite scores, but to reconsider their role altogether.

Evaluation should not aim to compress research into a single number.

It should aim to understand it in its full complexity.