arxiv: 2604.22811 · v1 · submitted 2026-04-14 · 💻 cs.DL

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Expertise Indices: Variants, Modifications, Advancements, and Computational Tools in R

Abhirup Nandy , Nilabhra R. Das

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:35 UTC · model grok-4.3

classification 💻 cs.DL

keywords expertise indicesx-indexx_d-indexthematic expertiseresearch evaluationR packageperformance metricsinstitutional strength

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The pith

New x-index variants and the xxdi R package measure institutions' thematic research expertise beyond h- and g-indices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Funding bodies increasingly tie support to performance metrics instead of trust alone. Standard h- and g-indices track overall publication volume and impact yet miss the thematic focus of an institution's work. The paper fills this gap by presenting the x-index and x_d-index, their field-normalised and fractional forms, plus two new variants that adjust for category and variance biases. It adds the x_o-index as an overall expertise summary and supplies the xxdi R package with ready functions for all these calculations. Wider use of these tools lets evaluators and institutions track specialised research strengths more directly.

Core claim

The x-index, x_d-index and their bias-adjusted variants, including the recently introduced field-normalised and fractional versions together with the new category-adjusted x-index, inverse variance weighted x_d-index, and the novel x_o-index, quantify institutional research expertise by incorporating thematic content and resolvable biases; the xxdi R package implements simple functions for all of them so the wider community can apply the measures in practice.

What carries the argument

The family of x-index and x_d-index expertise indicators that quantify thematic research strength and apply explicit bias adjustments.

If this is right

Institutions gain metrics that reflect depth in specific research themes rather than raw volume alone.
Bias adjustments support fairer cross-field comparisons for performance-based funding.
The xxdi R package makes the full set of indices immediately computable without custom coding.
The x_o-index supplies a single summary statistic for overall institutional expertise.
These measures can sit alongside h- and g-indices for more complete evaluations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Funding panels could weight thematic-expertise scores when allocating resources to priority areas.
Integration with publication databases might allow automated expertise profiles for teams or departments.
Testing the indices against real grant outcomes or collaboration patterns would show whether the bias adjustments improve decisions.
The same logic could extend from institutions to individual researchers once the package is available.

Load-bearing premise

The new indices and their adjustments actually capture thematic expertise in a way that is meaningfully better than h- and g-indices.

What would settle it

A direct comparison in which the x_o-index or category-adjusted x-index predicts institutional funding success or thematic output better than the h-index across multiple fields.

Figures

Figures reproduced from arXiv: 2604.22811 by Abhirup Nandy, Nilabhra R. Das.

**Figure 1.** Figure 1: Overlap of keywords belonging to different categories. While the 𝑥-index utilises low-level publication 'keywords' and ranks them by citation counts, publication databases (such as WoS) also include higher-level 'categories' which represent broader thematic areas, that is, several nested keywords form a single category. Consequently, several keywords are highly likely to be members of the sets of keywords … view at source ↗

read the original abstract

In the academic landscape, scientific research has been primarily conducted through research institutions, which requires a massive influx of funds from various sources. Presently, these funding bodies have been moving from trust-based funding to performance-based evaluation systems for granting funds to the research bodies. This has led to the rise in popularity of various indices or statistics that measure institutional research strength or expertise. Institutional research expertise usually focuses on publication volume and its impact measured using the widely used h- and g-indices. However, these indices fail to capture the thematic expertise of research for institutions. To address this gap, two new expertise indicators, namely the x-index, the x_d-index, and bias-adjusted variants, the field-normalised x_d-index, and the fractional x_d-index, were introduced recently. Additionally, we propose two new variants, the category-adjusted x-index and the inverse variance weighted x_d-index, which further account for resolvable bias, and a novel statistic, the x_o-index, which acts as a measure of the overall research expertise. While several packages that calculate the traditional h- and g-indices exist, these novel expertise indices are yet to be included in such existing packages. The 'xxdi' R package provides simple functions that implement these expertise indices and their variants, enabling their utilisation by the wider research community. A stable version of the package is available on CRAN (https://doi.org/10.32614/CRAN.package.xxdi) and an in-development version on GitHub (https://github.com/nilabhrardas/xxdi).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper mainly ships an R package that implements a handful of incremental bias adjustments and one new overall measure on top of the authors' own prior x-index and x_d-index work.

read the letter

The core contribution is practical rather than foundational. The authors add a category-adjusted x-index, an inverse-variance weighted x_d-index, and a new x_o-index for overall expertise, then wrap the whole family (including earlier variants like field-normalised and fractional versions) in the xxdi package now on CRAN. That package lowers the barrier for anyone who wants to compute these numbers without writing their own code, and the GitHub development version adds transparency. The motivation section is clear about why h- and g-indices miss thematic concentration at the institutional level, and the stress-test note confirms the full text supplies the actual formulas and adjustment procedures without obvious internal contradictions. That is real, usable work for people who already track institutional output. The package itself is the strongest part; it turns definitional proposals into something others can run immediately. The soft spots are mostly about evidence. The paper stays at the level of definitions and implementation, with little shown on how the new adjustments change results on real datasets or whether they outperform simpler alternatives in practice. Without validation examples, sensitivity checks, or head-to-head comparisons, it is hard to judge whether the bias corrections deliver meaningful improvement or mainly add parameters. This is an honest extension of the authors' earlier line of work rather than a re-examination of the underlying measurement problem. Readers working in bibliometrics or science-policy evaluation who need ready tools for thematic expertise would get the most out of it. It is not a paradigm shift, but the concrete code and clear scope make it worth a referee's time. I would send it to peer review rather than desk-reject; a reviewer could usefully ask for validation sections without the core package contribution disappearing.

Referee Report

0 major / 2 minor

Summary. The manuscript introduces new bibliometric indices (x-index, x_d-index and bias-adjusted variants including field-normalised x_d-index and fractional x_d-index) to capture thematic research expertise of institutions, which the authors argue is not addressed by h- and g-indices. It proposes additional variants (category-adjusted x-index, inverse variance weighted x_d-index) and a novel overall measure (x_o-index), and supplies the 'xxdi' R package (available on CRAN and GitHub) with functions to compute them.

Significance. If the indices prove robust in practice, the work supplies a practical extension to existing bibliometric toolkits for performance-based research evaluation, particularly where thematic focus and bias adjustment matter. The open R package is a concrete deliverable that lowers the barrier to adoption and reproducibility.

minor comments (2)

Abstract: while the full text supplies the mathematical definitions and package functions, the abstract itself contains no formulas, worked examples, or validation metrics; adding one concise definition or small illustrative calculation would improve immediate accessibility without lengthening the abstract substantially.
Section on package implementation: the manuscript states that the package provides 'simple functions' but does not list the exported function names or their argument signatures; including a short table or code snippet of the main user-facing functions would aid readers who wish to apply the indices immediately.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of the manuscript, recognition of its potential significance for bibliometric toolkits, and recommendation of minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

Minor self-citation for base indices; new variants and package are definitional

full rationale

The paper defines new indices (x-index, x_d-index variants, x_o-index) and supplies an R package as direct mathematical and computational proposals to fill a stated gap left by h- and g-indices. Base indices are referenced as 'introduced recently,' indicating self-citation, but this is not load-bearing for the new variants or package functions. No equation reduces a claimed prediction to a fitted parameter or prior result by construction; the derivations are explicit definitions. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract provides no explicit free parameters, invented entities, or detailed axioms beyond the domain assumption that h- and g-indices fail to capture thematic expertise. The contribution is primarily definitional and implementational.

axioms (1)

domain assumption h- and g-indices fail to capture the thematic expertise of research for institutions
Explicitly stated in the abstract as the motivation for developing new indices.

pith-pipeline@v0.9.0 · 5587 in / 1372 out tokens · 72321 ms · 2026-05-10T13:35:55.590551+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 10 canonical work pages

[1]

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R -tool for comprehensive science mapping analysis. Journal of Informetrics , 11(4), 959 –975. https://doi.org/10.1016/J.JOI.2017.08.007

work page doi:10.1016/j.joi.2017.08.007 2017
[2]

R., & Nandy, A

Das, N. R., & Nandy, A. (2026). xxdi: An R Package for Evaluating Scholarly Expertise Indices for Institutional Research Assessment (Version 1.3.1) [Computer software]. https://cran.r- project.org/web/packages/xxdi/index.html

2026
[3]

Egghe, L. (2006). An improvement of the h-index: The g-index. ISSI Newsletter

2006
[4]

Franceschini, F., & Maisano, D. (2011). Structured evaluation of the scientific output of academic research groups by recent h-based indicators. Journal of Informetrics, 5(1), 64–

2011
[5]

https://doi.org/10.1016/j.joi.2010.08.003

work page doi:10.1016/j.joi.2010.08.003 2010
[6]

Gagolewski, M., & Cena, A. (2023). agop: Aggregation Operators and Preordered Sets (Version 0.2.4) [Computer software]. https://cran.r - project.org/web/packages/agop/index.html

2023
[7]

Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102

work page doi:10.1073/pnas.0507655102 2005
[8]

Huang, M., & Lin, C. -S. (2011). Counting methods & university ranking by H -index. Proceedings of the American Society for Information Science and Technology , 48(1), 1–6. https://doi.org/10.1002/meet.2011.14504801191

work page doi:10.1002/meet.2011.14504801191 2011
[9]

H., Nandy, A., & Singh, V

Lathabai, H. H., Nandy, A., & Singh, V. K. (2021a). Expertise -based institutional collaboration recommendation in different thematic areas. CEUR Workshop Proceedings, 2847
[11]

Lazaridis, T. (2010). Ranking university departments using the mean h -index. Scientometrics, 82(2), 211–216. https://doi.org/10.1007/s11192-009-0048-4

work page doi:10.1007/s11192-009-0048-4 2010
[12]

López-Illescas, C., de Moya -Anegón, F., & Moed, H. F. (2011). A ranking of universities should account for differences in their disciplinary specialization. Scientometrics, 88(2), 563–574. https://doi.org/10.1007/S11192-011-0398-6

work page doi:10.1007/s11192-011-0398-6 2011
[13]

Mendoza, M. (2021). Differences in Citation Patterns across Areas, Article Types and Age Groups of Researchers. Publications, 9(4), 47. https://doi.org/10.3390/publications9040047

work page doi:10.3390/publications9040047 2021
[14]

H., & Singh, V

Nandy, A., Lathabai, H. H., & Singh, V. K. (2023). x_d -index: An overall scholarly expertise index for the research portfolio management of institutions. Proceedings of ISSI 2023 – the 19th International Conference of the International Society for Scientometrics and Informetrics, 473–488. https://doi.org/10.5281/zenodo.8305585

work page doi:10.5281/zenodo.8305585 2023
[15]

H., & Singh, V

Nandy, A., Lathabai, H. H., & Singh, V. K. (2024). $${\varvec{x}}_{{\varvec{d}}}$$-index and its variants: A set of overall scholarly expertise diversity indices for the research portfolio management of institutions. Scientometrics, 129(10), 5937 –5962. https://doi.org/10.1007/s11192-024-05131-y

work page doi:10.1007/s11192-024-05131-y 2024
[16]

Y., & Rousseau, R

Ye, F. Y., & Rousseau, R. (2010). Probing the h-core: An investigation of the tail–core ratio for rank distributions. Scientometrics, 84(2), 431 –439. https://doi.org/10.1007/s11192 - 009-0099-6

work page doi:10.1007/s11192 2010