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This paper aims to discuss the significance of teacher authorship (jissen kiroku) developed during jugyo kenkyu. Specifically, it explores the structural conditions of jugyo kenkyu that enabled the flourishing of jissen kiroku.

To find how jissen kiroku developed in jugyo kenkyu, this paper settled triad of authors-text-readers as the analytical perspective. Disputes through 1960s–1980s are adequate to inquire because it can elucidate how readers read jissen kiroku, which is typically challenging to observe.

Jissen kiroku is a powerful tool for semantically preserving, reconstructing and consolidating professional values and knowledge in jugyo kenkyu with deepening connoisseurship. Voluntary educational research associations (VERAs) encourage teachers to write and read jissen kiroku to develop their professionalism, which also helped develop exclusive semantics within the field. These developments were possible due to the public nature of jissen kiroku, disseminated to lesson study (LS) actors, thereby strengthening discussions both inside and outside VERAs.

The paper proposes shift in views on educational science and emphasizes authorship as authority in that professionalism of teaching can be protected and elevated through authoring.

The significant roles of writing practice have not been explored enough. This paper finds the value of authorship in terms of public nature and openness to all teachers which enable the enhancement of professionalism of the LS field.

This article presents a case study on the Heritage Building Information Modeling (H-BIM) application in a historic village in Bursa, Turkey. The study addresses how tailor-made and highly structured H-BIM approaches can effectively be implemented in preservation applications for historic vernacular buildings in the rural architecture context.

Using inexpensive digital photogrammetry techniques tightly combined with an object-oriented BIM ontology, parametric meta-modeling and object/system propagation methods, the study employed a holistic H-BIM approach for capturing the materiality, building object behaviors and indigenous construction principles of a characteristic vernacular house that were synthesized in a parametric H-BIM model. The followed stages, steps and connected methods were systematized and articulated in a prototypical H-BIM implementation framework.

The study findings suggested that the developed parametric H-BIM approach can return effective results with the combined use of low-cost and practical digital photogrammetry with BIM methods. The flexibility and adaptability of the parametric H-BIM implementation framework facilitated the synthesis of a comprehensive H-BIM model and allowed an in-depth evaluation of local architectural heritage with its physical, spatial and environmental characteristics. The proposed H-BIM approach also provided significant documentation and system-specific assessment benefits for preserving the vernacular examples which are prone to extinction especially due to structural and systemic deterioration.

The study proposes a feasible, practical and replicable H-BIM implementation methodology for vernacular preservation applications. The knowledge-embedded H-BIM approach, flows and techniques presented in this study provide a holistic and systematic H-BIM framework – with the integrated use of digital photogrammetry and parametric meta-modeling methods – that has the potential for the democratization of H-BIM applications in education and practice.

The purpose of this paper is to detail a low-cost, low-maintenance publishing strategy aimed at unlocking the value of Linked Data collections held by libraries, archives and museums (LAMs).

The shortcomings of commonly used Linked Data publishing approaches are identified, and the current lack of substantial collections of Linked Data exposed by LAMs is considered. To improve on the discussed status quo, a novel approach for publishing Linked Data is proposed and demonstrated by means of an archive of DBpedia versions, which is queried in combination with other Linked Data sources.

The authors show that the approach makes publishing Linked Data archives easy and affordable, and supports distributed querying without causing untenable load on the Linked Data sources.

The proposed approach significantly lowers the barrier for publishing, maintaining, and making Linked Data collections queryable. As such, it offers the potential to substantially grow the distributed network of queryable Linked Data sources. Because the approach supports querying without causing unacceptable load on the sources, the queryable interfaces are expected to be more reliable, allowing them to become integral building blocks of robust applications that leverage distributed Linked Data sources.

The novel publishing strategy significantly lowers the technical and financial barriers that LAMs face when attempting to publish Linked Data collections. The proposed approach yields Linked Data sources that can reliably be queried, paving the way for applications that leverage distributed Linked Data sources through federated querying.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

This study explores the deep integration of digital technology and cultural heritage to promote the preservation and inheritance of cultural heritage. Focusing on Digital Cultural Heritage (DCH), this research investigates its key role in activating theoretical research and practical applications in cultural heritage.

This study conducted an extensive bibliometric analysis utilizing VOSviewer and Bibliometrix visualization software to meticulously examine DCH research. Insights were gleaned from a dataset comprising 2,997 DCH-related publications harvested from the Web of Science database.

The bibliometric analysis reveals several notable findings: driven by active contributions from Italy, China, Spain, and the USA, the number of DCH publications shows a linear upward trend. Consiglio Nazionale delle Ricerche in Italy emerges as a prominent institution, while the Journal of Cultural Heritage stands out as the most influential journal in the DCH field. Scholars such as Remondino, Guidi, Barazzetti, and Carrozzino have significantly impacted DCH research. Furthermore, an in-depth analysis of keyword co-occurrence networks elucidates six major research trajectories in the DCH field, covering various aspects from cultural heritage digitization to digital humanities.

The study emphasizes the value of global knowledge exchange, interdisciplinary collaboration, innovative technology applications, and digital content provision practices in advancing DCH research.

By delving into the multifaceted landscape of DCH research, this study brings forth original insights into the escalating trends, pivotal contributors, and burgeoning research directions.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Inspired by objective hermeneutics (Oevermann, 1984, 1996, 1999; Oevermann et al., 1979; Weller, 2010; Wohrab-Sahr, 2003) and qualitative validation (Adcock and Collier, 2001; Martis, 2006; Maxwell, 1992), the authors present this essay with the aim of proposing an objective hermeneutic approach to qualitative validation.

In order to develop this approach, the authors consider the contributions of Martis (2006) and Maxwell (1992) about theoretical–empirical validity, Adcock and Collier's propositions (2001) regarding the conceptualization and evaluation of phenomena through specific levels, tasks and stages of validation and the principles of objective hermeneutic interpretation proposed by Wohlrab-Sahr (2003).

Three main contributions are considered: theoretical–empirical validity (Martis, 2006; Maxwell, 1992); levels of validation – theoretical framework, systematized concept, indicators and results (Adcock and Collier, 2001); stages of validation – content validity, convergent validity and nomological validity (Adcock and Collier, 2001); and principles of objective hermeneutic interpretation – sequential interpretation, mental–experimental explanation of possible interpretations, preservation rule, literal character of interpretation, totality, reflection about knowledge used in the analysis and group of interpreters (Wohrab-Sahr, 2003). These contributions were related to establishing a framework that illustrates the proposed objective hermeneutic approach to qualitative validation.

The authors intend to offer to the scope of organization studies an alternative for validation, so that the voices of the researched can be heard. Furthermore, the authors seek to guide researchers as to how to respect and protect what is heard, in order to avoid any invasion of others' discourse.

With the aim of developing a conceptual framework which aims to facilitate semantic metadata interoperability, this paper explores overarching conceptual issues on how traditional library information organisation schemes such as online public access catalogues (OPACs), taxonomies, thesauri, and ontologies on the one hand versus Web 2.0 technologies such as social tagging (folksonomies) can be harnessed to provide users with satisfying experiences.

This paper reviews works in relation to current metadata creation, utilisation and interoperability approaches, focusing on how a social constructivist philosophical perspective can be employed to underpin metadata decisions in digital libraries. Articles are retrieved from databases such as EBSCO host and Emerald and online magazines such as D‐Lib and Ariadne. Books, news articles and blog posts that are deemed relevant are also used to support the arguments put forward in this paper.

Current metadata approaches are deeply authoritative and metadata deployments in digital libraries tend to favour an objectivist approach with focus on metadata simplicity. It is argued that unless information objects are enriched with metadata generated through a collaborative and user‐driven approach, achieving semantic metadata interoperability in digital libraries will remain difficult.

In this paper, it is indicated that the number of metadata elements (fields) constituting a standard has a direct bearing on metadata richness, which in turn directly affects semantic interoperability. It is expected that this paper will contribute towards a better understanding of harnessing user‐driven metadata.

As suggested in this paper, a conceptual metadata framework underpinned by a social constructivist approach substantially contributes to semantic interoperability in digital libraries.

Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.

This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.

The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.

This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors’ knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.

Moving images represent a category of material which has historically received short shrift in most libraries and archives. Film, video, and now digital images form a significant part of many library and archival collections, however, and can be found in many formats and genres. Despite the ubiquity of such media in cultural institutions, the majority of libraries and archives owning collections of moving images have neglected these holdings—with the specific exception of those few archives devoted primarily to the care and preservation of moving images.