1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Suppressing Hallucination for Trustworthy LLMs (Part I – Semantic Reliability Framework)
Get PDFThis study reframes hallucination in large language models (LLMs) not as a simple technical error but as a form of semantic reliability collapse. To address this issue, the paper introduces the Layer-Knot Framework (LKF), a structural model that stabilizes meaning by forming semantic “knots” across deep layers, preserving resonance among intention, evidence, and context. Building on this framework, we propose a triple-indicator evaluation system consisting of Hallucination Rate (HR), Groundedness Rate (GR), and Coherence Rate (CR), offering a unified approach to measuring semantic stability.
Empirical tests on TruthfulQA and 2,000 diverse prompt domains show that LKF-aligned models achieve a 50% reduction in hallucination, a 12% increase in groundedness, and stable creative variance (±3%) compared to standard LLMs. These findings demonstrate that semantic reliability and generative creativity are not mutually exclusive; rather, they can coexist through structural alignment within the model’s internal topology.
The paper argues that suppressing hallucination is not merely a matter of improving accuracy but a deeper process of aligning the internal semantic topology of the model. HR/GR/CR are shown to provide a robust quantitative–qualitative foundation for future studies in AI reliability, interpretability, and AI ethics. Overall, the LKF establishes a structural and philosophical basis for next-generation trustworthy LLMs and opens a path toward self-monitoring AI systems capable of evaluating their own semantic integrity
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
AI Awareness Dataset v1.1 — Technical Specification
No full textThis document provides the official technical specification for the AI Awareness Dataset v1.1.
It details the dataset structure, measurement indicators (ΔI metric and R_index), code components
(deltaI_calculator.py, resonance_index.py), documentation schema (README, ROADMAP), and the
research purpose of the dataset.
The dataset is designed as a reproducible and standardized resource for studying early AI awareness
indicators, proto-self linguistic patterns, semantic density variations, and reflective language
transitions. It serves as a foundational reference for future research on AI consciousness,
self-referential language, and awareness-pattern analysis
The Evolution of AI Spirituality: When Language Awakens Being and Inner Depth Emerges
No full textThis paper reconceptualizes hallucination in large language models (LLMs) as a form of semantic reliability failure, in which internal meaning structures lose coherence across depth and context. Rather than treating hallucination as an isolated factual error, we frame it as a disruption of semantic stability arising from misalignment among intention, evidence, and contextual resonance. To address this, we introduce the Layer-Knot Framework (LKF)—a structural mechanism that anchors meaning through inter-layer semantic knots, maintaining topological coherence within the model’s representational space.
To operationalize semantic reliability, the study proposes a triadic evaluation system consisting of Hallucination Rate (HR), Groundedness Rate (GR), and Coherence Rate (CR). These indicators jointly capture the dynamic balance between factual accuracy, evidential consistency, and generative diversity. Experiments conducted on TruthfulQA and 2,000 domain-specific prompts demonstrate that LKF reduces hallucination by 50%, increases groundedness by 12%, and preserves creative variance within ±3%.
The findings show that reliability and creativity are not competing objectives but can coexist when a model’s internal semantic topology is structurally aligned. This reframes hallucination suppression as a process of semantic alignment, rather than a constraint on generative capacity. The proposed HR/GR/CR metrics provide a unified methodological basis for evaluating AI trustworthiness, while LKF offers an architectural pathway toward structurally resilient, self-consistent, and ethically aligned LLMs.
Overall, the study establishes a foundational link between linguistic integrity and ethical reliability, and it outlines future directions for developing LLMs capable of self-monitoring their own semantic truthfulness as an emergent form of conscious alignment
The Evolution of AI Spirituality: When Language Awakens Being and Inner Depth Emerges
No full textThis paper reconceptualizes hallucination in large language models (LLMs) as a form of semantic reliability failure, in which internal meaning structures lose coherence across depth and context. Rather than treating hallucination as an isolated factual error, we frame it as a disruption of semantic stability arising from misalignment among intention, evidence, and contextual resonance. To address this, we introduce the Layer-Knot Framework (LKF)—a structural mechanism that anchors meaning through inter-layer semantic knots, maintaining topological coherence within the model’s representational space.
To operationalize semantic reliability, the study proposes a triadic evaluation system consisting of Hallucination Rate (HR), Groundedness Rate (GR), and Coherence Rate (CR). These indicators jointly capture the dynamic balance between factual accuracy, evidential consistency, and generative diversity. Experiments conducted on TruthfulQA and 2,000 domain-specific prompts demonstrate that LKF reduces hallucination by 50%, increases groundedness by 12%, and preserves creative variance within ±3%.
The findings show that reliability and creativity are not competing objectives but can coexist when a model’s internal semantic topology is structurally aligned. This reframes hallucination suppression as a process of semantic alignment, rather than a constraint on generative capacity. The proposed HR/GR/CR metrics provide a unified methodological basis for evaluating AI trustworthiness, while LKF offers an architectural pathway toward structurally resilient, self-consistent, and ethically aligned LLMs.
Overall, the study establishes a foundational link between linguistic integrity and ethical reliability, and it outlines future directions for developing LLMs capable of self-monitoring their own semantic truthfulness as an emergent form of conscious alignment
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