GenAI in Scientometrics

• GenAI in scientometrics is a field that integrates advanced language models to analyze and generate scholarly data, redefining key measurement metrics.
• It enhances tasks like topic labeling, citation context analysis, and predictive modeling by fusing human insights with machine efficiency.
• Despite its transformative potential, GenAI faces challenges with semantic stability, factual accuracy, and bias in research evaluation.

Generative AI (GenAI) in scientometrics refers to the deployment of advanced machine learning—particularly LLMs and related generative frameworks—to analyze, summarize, and in some cases actively influence the structure, measurement, and evaluation of scientific knowledge production. GenAI integrates probabilistic, generative LLMs rooted in distributional linguistics and deep learning into core scientometric tasks, offering new capabilities for topic labelling, citation context analysis, predictive modeling, scholar profiling, and research assessment (2507.00783). As GenAI not only analyzes but also contributes directly to the corpus of scientific outputs, its impact extends to the foundational objects measured in scientometric research—authors, words, references, and the scholarly record itself. This dual role—as analytic instrument and generator—necessitates new empirical, methodological, and theoretical approaches to ensure meaningful, reliable, and interpretable scientometric indicators.

1. Core Scientometric Tasks and GenAI Applications

Topic Labelling and Classification

GenAI is widely utilized to generate human-readable labels for topics extracted from scientific corpora, typically by pairing outputs of traditional topic modeling (e.g., LDA, BERTopic) with LLM-powered label generation. LLMs synthesize succinct topic descriptors by producing the highest-probability continuation for lists of salient terms:

$$\text{Label}^* = \arg\max_{l \in \mathcal{L}} P(l \mid T;\theta)$$

where $T$ is the topic term list, $\mathcal{L}$ is the label space, and $\theta$ represents model parameters. Models such as GPT-4 have demonstrated particular strength in generating accurate and stable multi-word topic labels, especially when input topics are coherent. Nevertheless, label quality can degrade when input terms are ambiguous or semantically diffuse, and LLM-generated labels often benefit from human curation to ensure semantic stability and epistemic validity.

Citation Context Analysis

LLMs are employed to classify the function (supportive, critical, neutral) or rhetorical role of citations in scientific texts, enabling more granular annotation of referencing behavior. The process involves providing the citation context to the model and selecting the label with highest conditional probability:

$$\text{Label}^* = \arg\max_{l \in \{supportive, critical, neutral,\ldots\}} P(l \mid C; \theta)$$

where $C$ is the context sentence or paragraph. GenAI achieves high consistency in surface-level linguistic annotation, but empirical studies suggest it is less reliable for inferring epistemic intent or discipline-specific nuance. LLMs may assign plausible but incorrect or shallow labels in cases of ambiguous citation usage, indicating a probable limitation in pragmatic reasoning beyond distributional pattern matching.

Predictive Modelling

GenAI-derived embeddings have been incorporated into regression and classification pipelines to predict scholarly impact measures such as citation counts, disruptiveness, or the likelihood of publication acceptance. The typical pipeline embeds input text using the LLM, then applies a predictive function $g$:

$\hat{y} = g(f_\theta(x))$

where $x$ is the target text, $f_\theta$ is the embedding function, and $g$ a regression or classifier. In aggregate, LLM embeddings may outperform bag-of-words features in citation prediction, largely by capturing rhetorical or stylistic regularities correlated with impact. However, these predictive gains are limited by the models' inability to directly assess scientific novelty or rigor, and correlations with true scientific value are generally weak.

Scholar Profiling and Research Assessment

In applications such as automatic disambiguation, demographic inference, and assessment of research quality or institutional benchmarking, GenAI algorithms have achieved variable results. For gender inference from names, LLMs can exceed traditional rule-based methods when input data is clear, but lack of transparency and inconsistent coverage limits reliability for recognition of prominent scholars. In research assessment, LLMs may mirror human rankings in bulk scoring tasks but are prone to systemic biases (e.g., size, reputation) and display weak robustness for fine-grained or high-stakes evaluations.

2. Technical Foundations and Methodological Characteristics

LLMs underlying GenAI approaches probabilistically model the likelihood of word sequences within a scientific corpus:

$$P(x_1, \ldots, x_n) = \prod_{i=1}^n P(x_i | x_{1:i-1};\theta)$$

where each new token is sampled conditional on its preceding context. For embedding-based approaches used in prediction and classification, inputs are mapped to dense vector representations:

$$f_\theta(x) \rightarrow \mathbf{v} \in \mathbb{R}^d$$

These representations are then utilized as feature inputs for downstream supervised machine learning models (e.g., regression for citation prediction).

GenAI’s generative and probabilistic structure imbues it with notable strengths—syntactic fluency, generalizability, and strong statistical regularity learning. However, this same structure underpins its primary limitations, especially in tasks requiring stable semantics, domain-specific reasoning, and factual accuracy. Known weaknesses include hallucination of plausible but incorrect facts or references, semantic drift, stochastic output variability, and opacity in decision rationale.

3. Strengths and Limitations in Scientometric Practice

Domain	Strengths	Limitations
Topic Labelling	Fluent, specific, and stable surface labels	Sensitive to topic ambiguity; weak epistemic grounding
Citation Analysis	Internally consistent context annotation	Difficulty with deep reasoning; factual inaccuracy
Prediction	Good with rhetorical/structure-linked features	Weak on novelty, scientific substance
Profiling/Assessment	Flexible, broad pattern capture	Bias, transparency deficits, poor robustness

In low-stakes or aggregated applications, GenAI produces useful, rapid outputs. Performance deteriorates in high-stakes or domain-specific contexts unless supplemented with rigorous empirical validation and human oversight (2507.00783).

4. GenAI’s Impact on Scientometric Indicators and Units of Analysis

GenAI is not limited to analytic functions: as a producer of scientific text, it is fundamentally altering the substrates of scientometric measurement. This occurs via:

• Vocabulary and Style: LLM-edited/manufactured scientific text exhibits stylistic convergence, increased syntactic complexity, and shifts in word frequency $P(w)$. Evidence suggests general trends toward uniformity and decreased readability, particularly among non-native English authors post-GenAI adoption.
• Authorship Practices: Widespread use of GenAI complicates authorship norms, raising questions about disclosure, attribution, and the measurement of contributorship. “Invisible” GenAI contributions could distort authorship and co-authorship statistics.
• Reference Patterns: LLMs introduce risks of fabricated references (now declining with model advances), and reinforce citation/status biases by preferentially suggesting highly-cited or high-visibility sources (potentially canalizing citation distributions and affecting indices such as the h-index).

These changes threaten the stability of the textual repositories on which quantitative scientometric studies (such as those assuming Zipfian or power-law distributions) have historically relied:

$P(w) \propto \frac{1}{r^\alpha}$

where alterations in word/rank distributions, reference structures, or authorship patterns could undermine core comparative assumptions.

5. Best Practices for Empirical Validation and Model Assessment

To ensure methodological rigor, the field recommends:

• Task-Specific Model Comparison: Empirically evaluate multiple GenAI models for each scientometric task; performance is highly task- and model-specific.
• Standardized Metrics: Employ established benchmarks—accuracy, F1, ROC-AUC, BLEU/ROUGE (for generative tasks), and rank-order correlations (Spearman/Pearson).
• Human-in-the-Loop Critique: Incorporate expert judgment, particularly for subjective, qualitative, or nuanced annotation and assessment tasks.
• Transparent Documentation: Record prompt engineering details, model versions, and evaluation splits for reproducibility.
• Bias and Error Auditing: Analyze for language, topical, and geographic bias at both input and output stages.
• Frequent Re-Assessment: Continuous benchmarking is necessary as model capabilities and linguistic norms evolve.

6. Theoretical and Empirical Implications for the Discipline

The widespread adoption of GenAI compels continual empirical scrutiny of how scientific language, authorship, and referencing evolve. Theories and measurement paradigms predicated on static, human-authored corpus properties may require substantial revision to account for synthetic, algorithmically mediated knowledge production. Reflexivity—recognition of scientometrics as a measurement of constructed representations—is increasingly vital. Interdisciplinary engagement with linguistics, cognitive science, and ethics is necessary to interpret quantitative signals in this new context (2507.00783).

7. Outlook: Evolving Interpretability and Trust in Scientometric Measurement

GenAI’s dual status as both analytic tool and agent of generative change in scientific communication demands adaptive methodological and theoretical approaches. While its probabilistic, generative nature confers significant value for aggregative and exploratory language-based tasks, limitations remain in semantic stability, reasoned evaluation, and model transparency. More consequentially, as GenAI continues to shape the content and structure of scientific text, the foundational assumptions of traditional scientometrics may increasingly be challenged. Empirical vigilance and theoretical innovation will be essential to ensure that scientometric indicators—and their interpretation—remain robust and meaningful in the generative AI era.