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This paper aims to expand the scope and mitigate the biases of extant archival indexes.

The authors use automatic entity recognition on the archives of the Dutch East India Company to extract mentions of underrepresented people.

The authors release an annotated corpus and baselines for a shared task and show that the proposed goal is feasible.

Colonial archives are increasingly a focus of attention for historians and the public, broadening access to them is a pressing need for archives.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

Based on Lasswell’s communication model, this study investigates how four categories of factors (i.e. the source, content, medium and receiver) conjointly affect the relational, financial and social outcomes (i.e. what effect) of CSA communication.

With a survey (N = 366), this study found configurations of core CSA communication factors leading to three different CSA communication outcomes.

While this study found multiways to yield three different CSA outcomes, combinational logic indicated the combined effects from source, content, medium and receiver. With content and medium, individuals’ connectedness (receiver) to a CSA issue is a core factor leading to a high level of purchase intention and issue advocacy. This study also found that message strategies (i.e. informativeness, factual tone, no promotional tone) are core factors leading to a high level of trust and issue advocacy.

With the theoretical guidance, this research contributes to strategic communication practice for various entities involved in advocacy communication by enabling an improved understanding of advocacy communication factors and triggering different communication outcomes.

As CSA communication involves multiple strategies, conventional research agenda focusing on correlational and path analysis approaches provide limited understanding of communication practice. To fill this void, this study adopts a configurational approach to understand current CSA communication practices holistically.

This paper aims to investigate how the technical logics of a World Bank-led performance management reform interacted with the social, political and historical logics within a developing country (DC) regulatory organisation. The institutional environment both within and outside the organisation was considered to understand the performance management reform experience.

An interview-based, longitudinal, qualitative case study approach was used to locate accounting in its technical, social and political space. A large regulatory organisation in Bangladesh was investigated as a case study to reveal how traditional organisational practices and public sector norms mediated a performance management reform. Informed by the institutional logics (IL) and economies of worth perspectives, interviews were used to locate IL at macro-level and associated organisational actors’ strategic responses that ultimately shaped the implementation of a performance management system (PMS).

This paper reveals how accounting, as a social and political practice, influences accountability reform within a regulatory organisation. It provides an account of both the processes and resultant practices of an accounting reform initiative. While a consultative and transparent performance management process was intended to enhance accountability, it challenged the traditional organisational authority structure and culture. The new PMS retained, modified and adjusted a number of its characteristics over time. These adjustments reflected an amalgamation of the influence of institutional pressures from powerful constituents and the ability of the local agents (managers) in negotiating and mediating the institutionalisation of a new PMS.

The findings of this paper carry major implications for policy makers, particularly with respect to the design of future reform programs on PMS.

This paper offers a theoretical mapping of IL and its organisation-level interpretations and practices. Thus, the authors locate power and influence at field and firm levels. The findings of this study reflect historical, political and cultural backgrounds of the case study organisation and how these contextual forces were active in shaping the meaning of reform logics. Though the institutional environment and agents were unique to the case study organisation, this research offers a “process generalisation” that reveals how a best practice PMS was translated and transformed by the traditional organisational practices in a DC regulatory context.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

Community-generated digital content (CGDC) is one of the UK’s prime cultural assets. However, CGDC is currently “critically endangered” (Digital Preservation Coalition, 2021) due to technological and organisational barriers and has proven resistant to traditional methods of linking and integration. The challenge of integrating CGDC into larger archives has effectively silenced diverse community voices within our national collection. Our Heritage, Our Stories (OHOS), funded by the UK’s AHRC programme Towards a National Collection, responds to these urgent challenges by bringing together cutting-edge approaches from cultural heritage, humanities and computer science.

Existing solutions to CGDC integration, involving bespoke interventionist activities, are expensive, time-consuming and unsustainable at scale, while unsophisticated computational integration erases the meaning and purpose of both CGDC and its creators. Using innovative multidisciplinary methods, AI tools and a co-design process, previously unfindable and unlinkable CGDC will be made discoverable in our virtual national collection.

There currently exists a range of disconnected, fragile and under-represented community-generated heritage which is at increasing risk of loss. Therefore, OHOS will work to ensure the survival and preservation of these nationally important resources, for the future and for our shared national collection.

As we dissolve barriers to create meaningful new links across CGDC collections and develop new methods of engagement, OHOS will also make this content accessible to new and diverse audiences. This will facilitate a wealth of fresh research while also embedding new strategies for future management of CGDC into heritage practice and training and fostering newly enriching, robust connections between communities and archival institutions.

This study aims to examine an emerging product–place co-branding marketing practice in China’s rural areas. The role of this practice in inclusive development is analyzed from the perspectives of value proposition innovation, market legitimacy, media coverage and brand value. Both research and practice indicate value proposition innovation to exert an important influence on brand value enhancement, but little is known about the mediating and moderating mechanisms underlying this relation.

A moderated mediation model is constructed to examine whether market legitimacy mediates the relationship between value proposition innovation and brand value. vWhether this mediating process is moderated by media coverage is also examined. The primary data are collected from semi-structured interviews and observations conducted with two common cases to develop proper scales for value proposition innovation and market legitimacy. The research includes 100 product–place co-brandings published by the Ministry of Agriculture and Rural Affairs in 2019. Hypotheses are tested using hierarchical regression and a Bootstrap model.

Value proposition innovation has a positive effect on brand value, and market legitimacy partially mediates this relationship. Media coverage positively moderates the relationship between value proposition innovation and market legitimacy, and positively moderates the mediating effect of market legitimacy; the higher the media coverage, the stronger the mediating effect of market legitimacy.

Based on data availability and accessibility, the study sample focused on indicators from 100 brands in 2019. If the Ministry of Agriculture and Rural Affairs discloses consecutive annual information for other years, future studies could explore panel data to further test the study’s conclusions from a longitudinal perspective.

First, this paper adds to the emerging literature on product–place co-branding business models by examining the relationship between value proposition innovation and brand value. Second, this paper enriches institutional theory by including market legitimacy as a mediator between value proposition innovation and brand value. Third, this paper identifies the moderating role of media coverage, thus broadening the theoretical implications of institutional theory with respect to improving market legitimacy.

There is a wealth of value hidden in regional cultural heritage, but its preservation status is not optimistic. This study introduces a method that focuses on the inherent cultural value of regional cultural heritage to preserve it by value construction and release.

Based on the great value of regional cultural heritage due to spatial adjacency and temporal continuity, this paper focuses on its inherent cultural value to explore the preservation path and chooses Shichahai cultural heritage digital resources for a case study. This paper draws lessons from the narrative method of ancient Chinese historiography, constructs a cultural space and tells cultural stories. A linked data organization model for digital resources is created to construct a conceptual cultural space. Then, the space is materialized by linked dataset creation. The authors tell cultural stories discovered from the space, which are presented by various user interfaces using visualization technologies.

A cultural space promotes the development of a fine-grained description of regional cultural heritage and aids in relationship discovery to enhance the value construction ability. Additionally, storytelling via interactive user interfaces is helpful in the utilization and dissemination of knowledge extracted from a cultural space and enhances the value release of regional cultural heritage. In this way, a path with the inherent cultural value of regional cultural heritage as the core is established, and preservation is achieved.

This study focuses on the inherent cultural value of regional cultural heritage and proposes a new path to preserve these resources. This approach will enrich research on the preservation of regional cultural heritage and contribute to the construction and release of its cultural value.