In this article, we classify irregular threefolds with numerically trivial canonical divisors in positive characteristic. For a threefold, if its Albanese dimension is not maximal, then the Albanese morphism will induce a fibration which either maps to a curve or is fibered by curves. In practice, we treat arbitrary dimensional irregular varieties with either one-dimensional Albanese fiber or one-dimensional Albanese image. We prove that such a variety carries another fibration transversal to its Albanese morphism (a “bi-fibration” structure), which is an analog structure of bielliptic or quasi-bielliptic surfaces. In turn, we give an explicit description of irregular threefolds with trivial canonical divisors.

Debate about borderline personality disorder (BPD) has intensified, with some proposing its absorption into complex post-traumatic stress disorder and others questioning whether the diagnosis is harmful. These debates often obscure the central issue of construct validity. This paper evaluates whether BPD constitutes a coherent clinical entity. Drawing on Robins and Guze’s classic diagnostic validators – symptom specificity, heritability, course of illness, biological markers and treatment response – the evidence demonstrates that BPD is a robustly validated psychiatric disorder that should be retained in future classification systems. Concerns about stigma and dimensional models are considered but do not undermine its empirical grounding.

The application of a contract involves ascertaining whether the components of a contract term are met on the facts. It is a matter of categorisation or classification. There are at least three methods of categorisation: by criteria; by factor-balancing; and by analogy. The process of application is distinct from the processes that are engaged to define contract terms, including in particular interpretation. However, both the process of application and the process of interpretation address problems of linguistic indeterminacy, that is, cases where the words do not fit the facts. And these problems are usually resolved through interpretation; the relevant term is defined with such specificity that it is clear how it is to be applied. It is only when the court cannot define the term precisely that the court engages in categorisation or classification in a complex and meaningful way.

While comparative research on nonprofit organizations has made much progress since the launch of the Johns Hopkins Comparative Nonprofit Sector Project in 1990, there now seems to be a loss of momentum. Some of the reasons for this have to do with aspects of definition, classification, and aggregation that can be corrected. The main issue, however, is the lack of progress in advancing comparative nonprofit sector theories beyond the social origins theory. To remedy this, the essay proposes four ways forward as part of a new research agenda.

National Taxonomy of Exempt Entities (NTEE) codes have become the primary classifier of nonprofit missions since they were developed in the mid-1980s in response to growing demands for a taxonomy of nonprofit activities (Herman in Nonprofit and Voluntary Sector Quarterly 19(3):293–306, 1990, Barman in Social Science History 37:103–141, 2013). However, the increasingly complex nature of nonprofits means that NTEE codes may be outdated or lack specificity. As an alternative, scholars and practitioners can create a bespoke taxonomy for a specific purpose by hand-coding a training dataset and using machine learning classifiers to apply the codes to a large population. This paper presents a framework for determining training set sizes needed to scale custom taxonomies using machine learning algorithms.

Women and girls’ organisations (WGOs) are a fundamental part of the voluntary and community sector (VCS) and wider society. They have a unique history and development as a sub-set of the wider VCS and have been pivotal in meeting important needs for women and girls, yet existing analysis of the VCS using data from the Charity Commission for England and Wales (CCEW) has not previously focused on WGOs. The purpose of this paper is to present a feminist approach to identifying and classifying WGOs utilising data from the CCEW register. The paper provides a detailed exploration of the methodological approach and argues that such an approach to compiling and analysing datasets is both necessary and advantageous in exposing areas of power and contestation. The paper provides an original overview of the scope of WGOs in England and Wales between 2008 and 2018 and also demonstrates benefits that can be applied in other classification work within the field.

Relatively little is known about the Malaysian third sector. This is in large part due to the lack of large-scale data about the organisations that make up the sector, with the last comprehensive investigation nearly 50 years ago (Douglas and Pedersen in Blood, believer, and brother: the development of voluntary associations in Malaysia, 1973). The limited understanding of the make-up of the sector creates difficulties in policy development and resource allocation. For the first time, we combine the organisational databases of seven different regulators to map the Malaysian third sector, classifying organisations according to the International Classification for Non-Profit Organisations. We produce a map of the Malaysian third sector, describing its constituents, activities and beneficiaries. Our results show a sector cross-cut with ethnicity and religion, and we reflect on the implications both for the development of third sector organisations in Malaysia and for how current nonprofit theories adequately describe third sectors in non-western contexts.

There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report our experiences using two semi-automated methods based on textual data: rule-based classification and machine learning with curated keywords. We use those methods to classify Austrian nonprofit organizations based on the International Classification of Nonprofit Organizations. Those methods can provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, long high-quality texts describing NPOs’ activities are mostly unavailable, and human labor resources are limited. We find that in such a setting, rule-based classification performs about as well as manual human coding in terms of precision and sensitivity, while being much more labor-saving. Hence, we share our insights on how to efficiently implement such a rule-based approach. To address scholars with a background in data analytics as well as those without, we provide non-technical explanations and open-source sample code that is free to use and adapt.

How do political parties balance policy adaptation with ideological continuity? While spatial models emphasise external factors driving party behaviour, less attention has been given to internal party choices. This article innovates by proposing that parties differentiate between and make distinct strategical decisions regarding their ideological core and peripheral policy areas. We argue that parties maintain continuity in their ideological core while exhibiting greater flexibility in modifying their periphery. Using the Manifesto Project Corpus and an XLM-RoBERTa-based language model, we analyse manifesto sections to distinguish core from peripheral segments. Our findings show that parties take clearer stances in their ideological core while adapting their periphery more flexibly, with niche parties displaying this pattern more strongly than mainstream parties. Electoral setbacks lead parties to adopt more extreme peripheries, while cores remain stable. These results highlight the strategic importance of the core–periphery distinction in party communication and suggest that studies of party competition should consider where policy shifts occur within parties’ electoral programmes.