Showing papers in "Social Work in 2016"

Knowledge graph refinement: A survey of approaches and evaluation methods

Abstract: In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.

Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web

Abstract: One of the major barriers to the deployment of Linked Data is the difficulty that data publishers have in determining which vocabularies to use to describe the semantics of data. This system report describes Linked Open Vocabularies (LOV), a high quality catalogue of reusable vocabularies for the description of data on the Web. The LOV initiative gathers and makes visible indicators that have not been previously harvested such as the interconnections between vocabularies, version history along with past and current referent (individual or organization). LOV goes beyond existing Semantic Web vocabulary search engines and takes into consideration the value's property type, matched with a query, to improve vocabulary terms scoring. By providing an extensive range of data access methods (SPARQL endpoint, API, data dump or UI), we try to facilitate the reuse of well-documented vocabularies in the Linked Data ecosystem. We conclude that the adoption in many applications and methods of LOV shows the benefits of such a set of vocabularies and related features to aid the design and publication of data on the Web.

Sparklis: An expressive query builder for SPARQL endpoints with guidance in natural language

Abstract: SPARKLIS is a Semantic Web tool that helps users explore and query SPARQL endpoints by guiding them in the interactive building of questions and answers, from simple ones to complex ones. It combines the fine-grained guidance of faceted search, most of the expressivity of SPARQL, and the readability of (controlled) natural languages. No knowledge of the vocabulary and schema are required for users. Many SPARQL features are covered: multidimensional queries, union, negation, optional, filters, aggregations, ordering. Queries are verbalized in either English or French, so that no knowledge of SPARQL is ever necessary. All of this is implemented in a portable Web application, SPARKLIS, and has been evaluated on many endpoints and questions. No endpoint-specific configuration is necessary as the data schema is discovered on the fly by the tool. Online since April 2014, thousands of queries have been formed by hundreds of users over more than a hundred endpoints.

The Document Components Ontology (DoCO)

Abstract: The availability in machine-readable form of descriptions of the structure of documents, as well as of the document discourse (e.g. the scientific discourse within scholarly articles), is crucial for facilitating semantic publishing and the overall comprehension of documents by both users and machines. In this paper we introduce DoCO, the Document Components Ontology, an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements to describe both structural and rhetorical document components in RDF. In addition to describing the formal description of the ontology, this paper showcases its utility in practice in a variety of our own applications and other activities of the Semantic Publishing community that rely on DoCO to annotate and retrieve document components of scholarly articles.

The YASGUI family of SPARQL clients

Abstract: The size and complexity of the Semantic Web and its technology stack makes it difficult to query. Access to Linked Data could be greatly facilitated if it were supported by a tool with a strong focus on usability. In this paper we present the YASGUI family of SPARQL clients, a continuation of the YASGUI tool introduced more than two years ago. The YASGUI family of SPARQL clients enables publishers to improve ease of access for their SPARQL endpoints, and gives consumers of Linked Data a robust, feature-rich and user friendly SPARQL editor. We show that the YASGUI family had significant impact on the landscape of Linked Data management: YASGUI components are integrated in state-of-the-art triple-stores and Linked Data applications, and used as front-end by a large number of Linked Data publishers. Additionally, we show that the YASGUI web service - which provides access to any SPARQL endpoint - has a large and growing user base amongst Linked Data consumers.

Semantic Web for the Legal Domain: The'next step

Abstract: Ontology-driven systems with reasoning capabilities in the legal field are now better understood. Legal concepts are not discrete, but make up a dynamic continuum between common sense terms, specific technical use, and professional knowledge, in an evolving institutional reality. Thus, the tension between a plural understanding of regulations and a more general understanding of law is bringing into view a new landscape in which general legal frameworks – grounded in well-known legal theories stemming from 20th-century c. legal positivism or sociological jurisprudence – are made compatible with specific forms of rights management on the Web. In this sense, Semantic Web tools are not only being designed for information retrieval, classification, clustering, and knowledge management. They can also be understood as regulatory tools, i.e. as components of the contemporary legal architecture, to be used by multiple stakeholders – front-line practitioners, policymakers, legal drafters, companies, market agents, and citizens. That is the issue broadly addressed in this Special Issue on the Semantic Web for the Legal Domain, overviewing the work carried out over the last fifteen years, and seeking to foster new research in this field, beyond the state of the art.

Assessing the National School Social Work Practice Model: Findings from the Second National School Social Work Survey

Abstract: The Second National School Social Work Survey in 2014 aimed to update knowledge of school social work practice by examining how practitioner characteristics, practice context, and practice choices have evolved since the last national survey in 2008. This second survey was also developed to assess how the new national school social work practice model created by the School Social Work Association of America aligns with early 21st century school social work practice realities. The second survey was conducted from February through April 2014 (3,769 total responses were collected) and represents the largest sample of American school social workers surveyed in two decades. Data from the Second National School Social Work Survey showed a field that still has not fully responded to calls to implement evidence-informed and data-driven practices. This article notes the need to better integrate pre- and postservice training in data-driven practices and provides recommendations for ways to overcome barriers that school social workers report facing.

The debates of the European Parliament as Linked Open Data

Abstract: The European Parliament represents the citizens of the member states of the European Union (EU). The accounts of its meetings and related documents are open data, promoting transparency and accountability, and are used as source data by researchers. However, the official portal of these documents provides limited search facilities. This paper presents LinkedEP, a Linked Open Data translation of the verbatim reports of the plenary meetings of the European Parliament. These data are integrated with a database of political affiliations of the Members of Parliament, and enriched with detected topics from the EU's topic hierarchy and links to four other Linked Open Datasets. The results of this work are available through a SPARQL endpoint and a user interface with extensive browse and search facilities. It is now possible to combine in one query the time and topic of the debate, the spoken words - in any available translation - and information about the speaker uttering these, such as affiliations to countries, parties and committees. This paper discusses the design and creation of the vocabulary, data and links, as well as known use of the data.

Professional Resistance in Social Work: Counterpractice Assemblages

Abstract: The goal of this article is to deepen understanding of the concept of professional resistance. Studies show that social workers in various parts of the world are increasingly confronted with regulations, programs, and policies that challenge their ability to carry out their professional mission in an ethical manner. Social workers may also find themselves under the pressure of periodic retrenchment resulting from budgetary constraints and subjected to worsening working conditions and threats of wage or social benefit reduction. Therefore, it is not surprising that social workers are sometimes required to engage in actions to oppose these negative realities or, in other words, to practice professional resistance. However, despite its growing relevance, the term "professional resistance" remains both theoretically obscure and marginal to social work practice. This article traces the presence of the concept in social work history, examines divergent uses of the concept in social work literature, introduces theoretical perspectives that may help practitioners enlarge their professional repertoire, provides concrete cases of resistance in different contexts, and finally proposes some paths to professional resistance.

Visualisation of Linked Data – Reprise

Abstract: Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, interrelated datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data.

Names are not good enough: Reasoning over taxonomic change in the Andropogon complex

Abstract: We present a novel, logic-based solution to the challenge of reconciling the meanings of taxonomic names across multiple biological taxonomies. The challenge arises due to limitations inherent in using type-anchored taxonomic names as identifiers of granular semantic similarities and differences being expressed in original and revised taxonomic classifications. We address this challenge through: (1) the use of taxonomic concept labels - thereby individuating name usages according to particular sources and allowing each taxonomy to be recognized separately; (2) sets of user-provided Region Connection Calculus articulations among concepts (RCC-5: congruence, proper inclusion, inverse proper inclusion, overlap, exclusion); and (3) the use of an Answer Set Programming-based reasoning toolkit that ingests these constraints to infer and visualize consistent multi-taxonomy alignments. The feasibility of this approach is demonstrated with a use case involving pairwise alignments of 11 non-congruent classifications of Eastern United States grass entities variously assigned to the Andropogon glomeratus- virginicus 'complex' over an interval of 126 years. Analyses of name:meaning identity reveal that, on average, taxonomic names are reliable identifiers of taxonomic non-/congruence for approximately 60% of the 127 merge regions obtained in 12 pairwise alignments. The name:meaning cardinality over the entire time interval ranges from 1:6 to 4:1, with only 1:36 names attaining the semantically ideal 1:1 ratio. We discuss the applicability of the RCC-5 alignment approach in the context of achieving logic-based integration of non-/congruent taxonomic concept hierarchies in dynamic biodiversity data environments.

Toward Complete Inclusion: Lesbian, Gay, Bisexual, and Transgender Military Service Members after Repeal of Don't Ask, Don't Tell

Abstract: The 2010 repeal of Don't Ask, Don't Tell (DADT) is one example of how US public policy has shifted toward greater inclusion of lesbian, gay, and bisexual (LGB) individuals The repeal of DADT reversed the practice of discharging LGB service members on the basis of sexual identity LGB service members may now serve their country without fear of direct repercussions stemming from sexual identity Though it is a statutory step toward parity, DADT repeal does not address a number of cultural and institutional inequities that continue to hinder full inclusion of sexual minority service members Notably, as discussed in this article, DADT largely ignores issues facing the transgender population This study examines remaining inequities and their ramifications for lesbian, gay, bisexual, and transgender service members and their families The article concludes with practice and policy recommendations for culturally competent social work practice with military service members across the sexual identity spectrum

Ontology for observations and sampling features, with alignments to existing models

Abstract: We introduce new OWL ontologies for observations and sampling features, based on the O&M conceptual model from OGC and ISO 19156. Previous efforts, (a) through the W3C SSN project, and (b) following ISO rules for conversion from UML, had dependencies on elaborate pre-existing ontologies and frameworks. The new ontologies, known as om-lite and sam- lite, remove such dependencies, and can therefore be used with minimal ontological commitment beyond the O&M conceptual model. Time and space concepts, for which there are multiple existing solutions, are implemented as stub-classes, and patterns for linking to the existing ontologies are described. PROV is used to support certain requirements for the description of specimens. A more general alignment of both observation and sampling feature ontologies with PROV is described, as well as mappings to some other observation models and ontologies.

TraitBank: Practical semantics for organism attribute data

Abstract: Encyclopedia of Life (EOL) has developed TraitBank (http://eol.org/traitbank), a new repository for organism attribute (trait) data. TraitBank aggregates, manages and serves attribute data for organisms across the tree of life, including life history characteristics, habitats, distributions, ecological relationships and other data types. We describe how TraitBank ingests and manages these data in a way that leverages EOL's existing infrastructure and semantic annotations to facilitate reasoning across the TraitBank corpus and interoperability with other resources. We also discuss TraitBank's impact on users and collaborators and the challenges and benefits of our lightweight, scalable approach to the integration of biodiversity data.

PPROC, an ontology for transparency in''public procurement

Abstract: Public procurement or tendering refers to the process followed by public authorities for the procurement of goods and services. In most developed countries, the law requires public authorities to provide online information to ensure competitive tendering as far as possible, for which the adequate announcement of tenders is an essential requirement. In addition, transparency laws being proposed in such countries are making the monitoring of public contracts by citizens a fundamental right. This paper describes the PPROC ontology, which has been developed to give support to both processes, publication and accountability, by semantically describing public procurement processes and contracts. The PPROC ontology is extensive, since it covers not only the usual data about the tender, its objectives, deadlines and awardees, but also details of the whole process, from the initial contract publication to its termination. This makes it possible to use the ontology for both open data publication purposes and for the overall management of the public contract procurement process.

Depression among Muslims in the United States: Examining the Role of Discrimination and Spirituality as Risk and Protective Factors

Abstract: Depression is a widespread challenge that affects people in all cultures. Yet, despite the growth of the Muslim population in the United States, little research has been conducted on this topic with members of this cultural group. To address this gap in the literature, the present study examines the effect of discrimination and spirituality on depression with a sample of self-identified Muslims (N = 269). Consistent with our expectations, discrimination was a risk factor and spirituality was a protective factor. For instance, Muslims who reported being called offensive names were more likely (odds ratio [OR] = 3.39, 95% confidence interval [CI] = 1.82, 6.32) to report clinically significant levels of depressive symptoms compared with those who were not called offensive names, whereas saying daily prayers was associated with a lower likelihood of reporting elevated levels of symptoms (OR = 0.74, 95% CI = 0.55, 0.97). The article concludes with a discussion of the implication of the results as they intersect social work practice and of avenues for future research.

A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Abstract: Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets.

Distantly supervised Web relation extraction for knowledge base population

Abstract: Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%.

Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Abstract: In this paper we examine the use of crowdsourcing as a means to master Linked Data quality problems that are difficult to solve automatically. We base our approach on the analysis of the most common errors encountered in Linked Data sources, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and compare different crowdsourcing approaches to identify these Linked Data quality issues, employing the DBpedia dataset as our use case: (i) a contest targeting the Linked Data expert community, and (ii) paid microtasks published on Amazon Mechanical Turk. We secondly focus on adapting the Find-Fix-Verify crowdsourcing pattern to exploit the strengths of experts and lay workers. By testing two distinct Find-Verify workflows (lay users only and experts verified by lay users) we reveal how to best combine different crowds’ complementary aptitudes in quality issue detection. The results show that a combination of the two styles of crowdsourcing is likely to achieve more efficient results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of Linked Data.

The Open University Linked Data - data.open.ac.uk

Abstract: The article reports on the evolution of data.open.ac.uk, the Linked Open Data platform of the Open University, from a research experiment to a data hub for the open content of the University. Entirely based on Semantic Web technologies (RDF and the Linked Data principles), data.open.ac.uk is used to curate, publish and access data about academic degree qualifications, courses, scholarly publications and open educational resources of the University. It exposes a SPARQL endpoint and several other services to support developers, including queries stored server-side and entity lookup using known identifers such as course codes and YouTube video IDs. The platform is now a key information service at the Open University, with several core systems and websites exploiting linked data through data.open.ac.uk. Through these applications, data.open.ac.uk is now fulfilling a key role in the overall data infrastructure of the university, and in establishing connections with other educational institutions and information providers.

Semantic Abstraction for generalization of tweet classification: An evaluation of incident-related tweets

Abstract: Social media is a rich source of up-to-date information about events such as incidents. The sheer amount of available information makes machine learning approaches a necessity to process this information further. This learning problem is often concerned with regionally restricted datasets such as data from only one city. Because social media data such as tweets varies considerably across different cities, the training of efficient models requires labeling data from each city of interest, which is costly and time consuming. To avoid such an expensive labeling procedure, a generalizable model can be trained on data from one city and then applied to data from different cities. In this paper, we present Semantic Abstraction to improve the generalization of tweet classification. In particular, we derive features from Linked Open Data and include location and temporal mentions. A comprehensive evaluation on twenty datasets from ten different cities shows that Semantic Abstraction is indeed a valuable means for improving general- ization. We show that this not only holds for a two-class problem where incident-related tweets are separated from non-related ones but also for a four-class problem where three different incident types and a neutral class are distinguished. To get a thorough understanding of the generalization problem itself, we closely examined rule-based models from our evalu- ation. We conclude that on the one hand, the quality of the model strongly depends on the class distribution. On the other hand, the rules learned on cities with an equal class distribution are in most cases much more intuitive than those induced from skewed distributions. We also found that most of the learned rules rely on the novel semantically abstracted features.

“The Pain of Exile”: What Social Workers Need to Know about Burmese Refugees

Abstract: Refugees from Burma have comprised the largest group of refugees resettling in the United States over the past decade, with nearly 90,000 people, and 19 percent of the total refugee population. However, very little literature exists that describes the cultural context and displacement experiences of this population. This article addresses that gap in the literature by examining historical, social, political, and cultural dimensions relevant to social work practice with Burmese refugees. Practice with Burmese refugees should be informed by knowledge of refugee policy, refugee resettlement, and social services delivery systems; the Burmese historical and political context; the community's specific strengths, needs, and cultural diversity; and human rights and social justice issues. Strong community partnerships between social workers and indigenous community leaders, between resettlement agencies and ethnic community-based organizations, and between different Burmese refugee groups are important to meeting short- and long-term social services needs and fostering successful adaptation and community integration.

U.S. Social Worker Workforce Report Card: Forecasting Nationwide Shortages

Abstract: Using age-based projected changes in population, the authors developed demand and supply models of the social worker workforce to project the shortage of social workers in all 50 states and assigned letter grades based on shortage ratios. According to the projections, the number of states with shortage ratios more severe than the current national ratio will increase from 11 states in 2012 to 30 states by 2030 and the nation will experience a total shortfall of over 195,000 social workers, with the most severe shortages occurring in the western and southern regions of the United States. Further efforts are recommended to investigate shortage dynamics and develop strategies to counter its causes.

CEDAR : The Dutch Historical Censuses as Linked Open Data

Abstract: In this document we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses, conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. The dataset contains more than 6.8 million statistical observations about the demography, labour and housing of the Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube vocabulary for multidimensional data, uses Open Annotation to express rules of data harmonization, and keeps track of the provenance of every single data point and its transformations using PROV. We link these observations to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC), which in turn link to DBpedia and GeoNames. The two main contributions of the dataset are the improvement of data integration and access for historical research, and the emergence of new historical data hubs, like classifications of historical religions and historical house types, in the Linked Open Data cloud.

JRC-Names: Multilingual entity name variants and titles as Linked Data

Abstract: Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities ( persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/ Binyamin/Bibi/Benyamin/Biniamin/Netanyahu/Netanjahu/Neanyahou/Netahny/ ). This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.

Voices from Social Work Graduates in China: Reasons for Not Choosing Social Work as a Career

Abstract: Although previous studies have addressed turnover issues after being a social worker, this study identifies factors that may block initial entry to the profession. Using a semistructural interview method with 20 BSW graduates, the researchers transcribed the reasons for BSW graduates not entering a career in social work. Through element-centered content analysis, 76 reasons were sorted into nine categories: (1) income insufficient for basic needs, (2) unclear future, (3) no commitment to social work, (4) social work jobs could be taken by other professionals, (5) difficulties in actualizing proclaimed value, (6) personally unable to apply skills, (7) social exclusion due to nonresident status, (8) hard/stressful work, and (9) not supported by peers and family. Through person-centered content analysis, most respondents (90 percent) reported multiple reasons (M = 3.8) supporting their decision, offering their rational thought processes culminating in the decision not to enter social work. Recommendations for developing a national survey, engaging social workers in preparing BSW students for graduation, and modifying student admissions strategies are discussed.

Out-of-School Suspensions of Black Youths: Culture, Ability, Disability, Gender, and Perspective

Abstract: Racial disproportionality in out-of-school suspensions is a persistent social justice issue in public schools. This article examines out-of-school suspensions of four black youths from the perspectives of the youths, their caregivers, and educators. The case involving David, a 14-year-old African American with a learning disability, illustrates the challenges of students experiencing the intersection of disability and race. The case involving George, a 14-year-old Liberian immigrant, illustrates how parents and teachers may form alliances around shared goals and values despite profound cultural differences in understanding of youths' misbehavior. The case involving Nina, a 12-year-old African American, illustrates how educators' failure to consider the context of her misbehaviors as responses to sexual harassment, along with their subsequent harsh punishment and failure to protect her, led to her disengagement from school. The case involving Craig, a 16-year-old African American, provides a glimpse into how the use of criminal justice language to refer to youths' misbehaviors can support the development of a criminalized self- and social identity. These cases illustrate the diversity of black students--including ability, disability, culture, and gender--and how events surrounding suspensions are interpreted by students, caregivers, and educators. Understanding such diversity will undergird implementation of effective alternatives to suspensions.