Excellence Paradox: Multidimensional Evaluation Tensions

• Excellence Paradox is defined as recurring tensions where systems designed to reward excellence capture only partial value or even undermine performance.
• The topic reviews empirical evidence from scientometrics, research evaluation, and organizational studies, highlighting measurement limitations and counterproductive incentives.
• Analyses of metrics like AMT and Impact Vitality illustrate how proxy-based evaluations fail to capture the multidimensional and context-sensitive nature of excellence.

The Excellence Paradox denotes a family of recurring tensions in which systems designed to identify, reward, or optimize excellence fail to capture the intended object, capture only one of its dimensions, or generate outcomes that undermine the very performance they were meant to improve. In research evaluation, the paradox appears when excellence is highly valued but hard to define and even harder to measure with a single metric; in organizational and market settings, rewarding top performers can reduce efficiency, output, or profit; in AI and industrial tooling, high capability in production or prediction does not reliably imply high capability in evaluation, trustworthiness, or adoption (Rosenfled et al., 2023, 0907.0455, Oh et al., 2024, Shahedi et al., 2 Aug 2025). A general explanation advanced in the quality literature is that quality assessment faces an inherent conflict between validity and applicability and must operate in high-dimensional spaces where measurement is necessarily incomplete (Reich, 2016).

1. Scope and semantic range

The expression is used explicitly in some recent works and is also a useful umbrella for structurally similar paradoxes described under other names. Across domains, the recurrent pattern is that “excellence” is not a one-dimensional quantity and that optimization against a proxy, ranking, or institutional procedure can shift attention from substantive value to what is easiest to evaluate.

Domain	Paradoxical relation	Representative papers
Scientometrics	Excellence is sought, but no single bibliometric captures it adequately	(Rosenfled et al., 2023, Rons et al., 2013, Erkol et al., 2022)
Research evaluation and funding	Metrics may support some allocation tasks while failing at quality ranking; representability can replace epistemic value	(Opthof et al., 2011, Mryglod et al., 2012, Müller, 3 Feb 2026)
Organizations and markets	Promoting or rewarding the best can reduce efficiency, mobility, profit, or output	(0907.0455, Livan, 2019, Amir et al., 31 Aug 2025)
AI and industrial tooling	Systems may solve or compute well yet fail as judges or adopted tools	(Oh et al., 2024, Shahedi et al., 2 Aug 2025)

A distinct, non-paradoxical technical usage also exists in model theory. In quasiminimal excellent classes, “excellence” is a structural property related to categoricity; it was shown to be redundant because it follows from the other axioms, substantially simplifying the framework (Bays et al., 2012). This usage is terminologically important but conceptually separate from the evaluative and organizational paradoxes discussed elsewhere.

2. Scientometric formulations: multidimensional excellence and competing indicators

A central scientometric version of the paradox is the claim that academic excellence is multidimensional and therefore only partially visible through standard indicators. “The Academic Midas Touch” proposes AMT as the rate of papers in a scholar’s portfolio that become “golden” by reaching a citation threshold within a time window,

$$AMT(s) := \frac{1}{|P|}\sum_{p \in P} \mathcal{G}(p),$$

with a binary instantiation in which $\mathcal{G}(p)=1$ when a paper accumulates at least $y$ citations within the first $x$ years after publication. In the main analysis, the parameters are $x=3$ years and $y=15$ citations, using data on 8,468 mathematicians. Under this specification, AMT is approximately normally distributed, monotonic in citations, and distinguishes 100 award-winning mathematicians from an age-controlled sample of 100 non-winners: award winners have $0.45 \pm 0.06$ versus $0.38 \pm 0.04$, with a one-tailed t-test, $p=0.038$, whereas the H-index ($p=0.057$), i10-index ($\mathcal{G}(p)=1$0), and citation count ($\mathcal{G}(p)=1$1) do not separate the groups at conventional significance levels (Rosenfled et al., 2023). The same study reports only moderate correlations between AMT and standard metrics—$\mathcal{G}(p)=1$2 with H-index, $\mathcal{G}(p)=1$3 with i10-index, and $\mathcal{G}(p)=1$4 with citations—supporting the claim that AMT captures a distinct aspect of scholarly performance (Rosenfled et al., 2023).

A related dynamic indicator is Impact Vitality, proposed for identifying excellent individual scientists by weighting citing publications inversely by age, so that more recent uptake counts more heavily. The intended interpretation is simple: $\mathcal{G}(p)=1$5 indicates increasing citing activity over time, $\mathcal{G}(p)=1$6 decreasing activity, and values around $\mathcal{G}(p)=1$7 no clear trend. In an initial test on applicants for senior research fellowships at VUB, all selected applicants had $\mathcal{G}(p)=1$8 for all years since the PhD, whereas none of the rejected applicants did so throughout (Rons et al., 2013). This formulation operationalizes excellence as ongoing relevance and increasing uptake rather than accumulated stock alone.

Another strand asks whether excellence lies in quantity, in a few spectacular hits, or in sustained high-quality output. A portfolio analysis of Nobel Prize laureates finds that indicators rewarding balanced impact perform best. The parametric Citation Moment

$\mathcal{G}(p)=1$9

achieves its best discrimination when the optimal $y$0 is below 1, which favors balanced portfolios rather than a small number of extreme papers. The parameter-free E-index,

$y$1

is high when citations are both large on average and spread across many papers. In full-portfolio classification of Nobelists versus baseline scientists, the reported AUC-PR values are 0.43 and 0.44 in Physics for $y$2 and $y$3, 0.49 and 0.53 in Chemistry, and 0.78 and 0.75 in Medicine (Erkol et al., 2022). In matched comparisons controlling for number of papers and total citations, Nobelists more often have the larger E-index: 58.2% in Physics, 86.3% in Chemistry, and 87.5% in Medicine (Erkol et al., 2022).

The literature does not converge on a single portfolio shape. A 2025 study argues that excellence can also appear as extreme inequality within a scientist’s own citation distribution. Using Google Scholar data from 126,067 researchers with at least 100 publications and 80 Nobel laureates, it reports that citation distributions are highly unequal and even more unequal among Nobel laureates; the laureates cluster near a high-inequality regime with

$y$4

where $y$5 is the Gini index and $y$6 the Kolkata index (Biswas et al., 11 Mar 2025). This suggests that excellence can be operationalized either as balanced high-impact output or as concentrated success, depending on whether the object of interest is consistency across papers or the structure of competitive concentration.

3. Validation limits in research evaluation and funding

A sharper version of the paradox concerns validation. A correspondence on CWTS-style citation indicators argues that stability is not validity: old and new “crown” indicators may correlate strongly with each other, yet still fail to correlate with peer-review-based quality. Using Van Raan’s dataset of 147 chemistry and chemical engineering research groups in the Netherlands, the study reports that peer-review quality is not significantly correlated with CPP/JCSm, CPP/FCSm, or the h-index, and that these indicators cannot reliably discriminate between groups rated “good” and “excellent” because the observed differences lie within standard errors (Opthof et al., 2011). The implication is not that citation analysis is useless, but that it cannot by itself legitimate the strategic selection of excellence.

A complementary result appears when the evaluation target is changed from quality per head to total institutional strength. In UK biology departments, peer-reviewed specific quality $y$7 and citation-based specific impact $y$8 are only moderately aligned, with Pearson correlation $y$9; by contrast, the absolute quantities

$x$0

correlate extremely strongly, with $x$1 (Mryglod et al., 2012). The paradox is therefore policy-specific: citation indicators may be suitable for funding allocations tied to total strength while being unsuitable for ranking by quality.

At the level of higher-education systems, the paradox appears as internal heterogeneity. A bibliometric analysis of Italian universities in the hard sciences during 2004–2008 finds that excellent disciplines are not concentrated in a few uniformly elite institutions. Among the 12 universities in overall Class A, only the first five are top in all active UDAs; lower-ranked universities can still host excellent disciplines. The standardized mutual variability index $x$2 for UDA performance within universities has an approximately Gaussian distribution, a modal range of 0.4–0.6, and median 0.455, indicating substantial internal dispersion (Abramo et al., 2018). Excellence is thus dispersed in pockets rather than consolidated institution-wide.

The funding literature extends this diagnosis from metrics to proposal evaluation itself. A 2026 practitioner account argues that “excellence has become decoupled from knowledge production through an increasing coupling to representability under evaluation,” so that systems increasingly select for “excellence-as-representability” rather than epistemic contribution (Müller, 3 Feb 2026). The paper identifies three reinforcing trends: professionalized proposal writing through consultants, AI-assisted applications, and evaluator shortages. In large consortia, coordination complexity scales as

$x$3

making it easier to optimize the appearance of integration in documents than genuine integration in practice (Müller, 3 Feb 2026). A plausible implication is that the paradox intensifies when evaluation must judge future knowledge before that knowledge exists.

4. Incentives, promotion, and counterproductive reward structures

In organizational theory, the best-known form of the paradox is the Peter Principle: excellence at one hierarchical level does not guarantee excellence at the next. In an agent-based model with 160 total positions across 6 levels, competence either transfers with small perturbation under the Common Sense hypothesis or is reassigned independently under the Peter hypothesis. The asymptotic results are counterintuitive. Under Common Sense, promoting the best yields about +9% efficiency, while under the Peter hypothesis it yields about −10%; by contrast, random promotion yields about +2% under Common Sense and about +1% under the Peter hypothesis, and promoting the worst yields about +12% under the Peter hypothesis (0907.0455). The paradox here is structural: a meritocratic rule degrades the organization when role competence is non-transferable.

A related mechanism operates in ranked societies. In a numerical model of agents choosing actions by either imitating better-ranked agents or exploring randomly, stronger imitation increases aggregate utility but also raises inequality, homogenizes behavior, and freezes rankings. Early lucky winners retain their advantage, so the ranking becomes less connected to intrinsic fitness and less meritocratic over time (Livan, 2019). Ranking, in this sense, does not merely record excellence; it feeds back into behavior and can consolidate accident into status.

Industrial-organization theory produces an even more formal inversion. In a Cournot duopoly with a fixed bonus awarded to the manager who earns the higher profit, the homogeneous-good contest has a unique unbeatable strategy

$x$4

and if both players use it,

$x$5

The contest thus drives the market to the point where price equals marginal cost and profits vanish, a form of Schaffer’s paradox (Amir et al., 31 Aug 2025). Under linear demand, returning from the contest outcome to ordinary Cournot competition reduces output by one-third, a “transformational recession” in the paper’s terminology (Amir et al., 31 Aug 2025). Incentive schemes aimed at relative superiority can therefore generate outcomes opposite to conventional intuitions about competition and efficiency.

A softer organizational version appears in the inclusion literature. “Unleashing Excellence through Inclusion” argues that inclusion and excellence are not opposites, but become oppositional when inclusion is treated as open-ended democracy. The proposed solution is “inclusion by design,” organized through eight factors within a PDCA logic, including expectation setting, working agreements, lean decision-making, assessment cadence, and quick incremental action (Radziwill et al., 2024). Excellence requires bounded participation, not universal participation in every decision.

5. AI and technology: capability without judgment or adoption

In AI evaluation, the paradox takes the form of a dissociation between generation and judgment. A study on TriviaQA uses 905 questions after filtering and compares GPT-3.5, GPT-4, PaLM-2, and Vicuna-13b at temperature = 0. Reported overall generation versus evaluation accuracies are approximately 0.79 versus 0.78 for GPT-3.5, 0.88 versus 0.87 for GPT-4, and 0.66 versus 0.64 for PaLM-2 (Oh et al., 2024). The more revealing result concerns questions that the evaluator itself solved correctly: even there, evaluation accuracy is only 80–90%, not 100% (Oh et al., 2024). The paper calls this unfaithful evaluation: a model may know the answer yet misjudge another model’s answer, or fail to solve the question yet still correctly judge another model’s response, with recall values of 0.73 for GPT-3.5, 0.78 for GPT-4, and 0.89 for PaLM-2 in the latter case (Oh et al., 2024). Generative excellence therefore does not imply evaluative faithfulness.

Industrial software tooling exhibits an analogous pattern. A year-long collaboration with Ericsson Montréal on TMLL finds that adoption barriers were not primarily raw technical sophistication but difficulty translating expert knowledge into actionable insights, integrating analysis into workflows, and trusting automated outputs. In a survey of 40 industry and academic professionals, 77.5% prioritized quality and trust in results over technical sophistication, and 67.5% preferred semi-automated analysis with user control (Shahedi et al., 2 Aug 2025). The paper derives three principles—cognitive compatibility, embedded expertise, and transparency-based trust—and states the paradox directly: technical excellence can actively impede adoption when it conflicts with usability, transparency, and practitioner trust (Shahedi et al., 2 Aug 2025).

6. General explanatory frameworks and continuing controversies

Several works converge on a common explanation: excellence is context-sensitive, multidimensional, and only partially observable. In the quality-theory literature, measurement confronts two fundamental limits. First, there is an inherent conflict between validity and applicability: narrowing context improves validity but reduces generality. Second, quality usually lives in high-dimensional spaces affected by the curse of dimensionality, so measurement alone cannot exhaust the object of interest (Reich, 2016). The proposed heuristic distinction between strategic qualities and necessary qualities formalizes why no universal scalar measure can fully capture excellence (Reich, 2016).

This framework helps explain why the paradox does not disappear even when systems are known to be noisy. A perspective on scientific practice argues that science has always produced outstanding results despite irreproducibility because it is a large-scale process of trial and error, filtering, and selective retention. The paper notes more than two million scientific publications each year, states that about half of all papers are never cited, and nevertheless argues that scientific advances outweigh the problems “by orders of magnitude” (Shiffrin et al., 2017). Excellence, on this view, does not require local perfection; it emerges from collective self-correction.

Taken together, the literature does not define a single theorem of the Excellence Paradox. Rather, it identifies a recurrent family of failure modes in which proxies, rankings, institutional procedures, and incentive mechanisms diverge from substantive value. Sometimes the problem is measurement incompleteness; sometimes proxy optimization; sometimes transfer failure across tasks or hierarchical levels; sometimes path dependence created by rankings; and sometimes adoption frictions ignored by technically oriented design. What remains stable across these variants is the central warning: systems built to recognize or maximize excellence often end up selecting what is easiest to count, compare, anticipate, or imitate, rather than what is most valuable in itself.