Background: Collaboration between Speech and Language Therapists (SLTs) and parents is considered best practice for children with developmental disorders. However, such collaborative approach is not yet implemented in therapy for children with developmental language disorders (DLD) in the Netherlands. Improving Dutch SLTs’ collaboration with parents requires insight in factors that influence the way SLTs work with parents. Aims: To explore the specific beliefs of Dutch SLTs that influence how they collaborate with parents of children with DLD. Methods and procedures: We conducted three online focus groups with 17 SLTs using a reflection tool and fictional examples of parents to prompt their thoughts, feelings and actions on specific scenarios. Data were organised using the Theoretical Domains Framework (TDF). Outcomes and results: We identified 34 specific beliefs across nine TDF domains on how SLTs collaborate with parents of children with DLD. The results indicate that SLTs hold beliefs on how to support SLTs in collaborating with parents but also conflicting specific beliefs regarding collaborative work with parents. The latter relate to SLTs’ perspectives on their professional role and identity, their approach towards parents, and their confidence and competence in working collaboratively with parents.
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Content moderation is commonly used by social media platforms to curb the spread of hateful content. Yet, little is known about how users perceive this practice and which factors may influence their perceptions. Publicly denouncing content moderation—for example, portraying it as a limitation to free speech or as a form of political targeting—may play an important role in this context. Evaluations of moderation may also depend on interpersonal mechanisms triggered by perceived user characteristics. In this study, we disentangle these different factors by examining how the gender, perceived similarity, and social influence of a user publicly complaining about a content-removal decision influence evaluations of moderation. In an experiment (n = 1,586) conducted in the United States, the Netherlands, and Portugal, participants witnessed the moderation of a hateful post, followed by a publicly posted complaint about moderation by the affected user. Evaluations of the fairness, legitimacy, and bias of the moderation decision were measured, as well as perceived similarity and social influence as mediators. The results indicate that arguments about freedom of speech significantly lower the perceived fairness of content moderation. Factors such as social influence of the moderated user impacted outcomes differently depending on the moderated user’s gender. We discuss implications of these findings for content-moderation practices.
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The field of data science and artificial intelligence (AI) is growing at an unprecedented rate. Manual tasks that for thousands of years could only be performed by humans are increasingly being taken over by intelligent machines. But, more importantly, tasks that could never be performed manually by humans, such as analysing big data, can now be automated while generating valuable knowledge for humankind
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From the introduction: "There are two variants of fronto-temporal dementia: a behavioral variant (behavioral FTD, bvFTD, Neary et al. (1998)), which causes changes in behavior and personality but leaves syntax, phonology and semantics relatively intact, and a variant that causes impairments in the language processing system (Primary Progessive Aphasia, PPA (Gorno-Tempini et al., 2004). PPA can be subdivided into subtypes fluent (fluent but empty speech, comprehension of word meaning is affected / `semantic dementia') and non-fluent (agrammatism, hesitant or labored speech, word finding problems). Some identify logopenic aphasia as a FTD-variant: fluent aphasia with anomia but intact object recognition and underlying word meaning."
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It was a landmark speech. On May 26, Angola’s president apologized and asked forgiveness for mass executions that occurred in 1977. He also announced the returning of victims’ remains to their families and the issuance of death certificates. But what does this mean for Angola’s wider reconciliation process? Other key demands and large groups of victims have still been left out, warn scholars Maarten van Munster and Joris van Wijk.
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Tot op heden bestond er geen onderzoeksinstrument dat betrouwbaar differentieert tussen broddelende en stotterende clienten. Een door de eerste auteur ontwikkelde test voor spraakmotorische controle op woordniveau is genormeerd en gevalideerd voor broddelende, stotterende sprekers en controles. Dit unieke instrument heeft een differentiaal diagnostische waarde binnen de vloeiendheidsstoornissen.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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What you don’t know can’t hurt you: this seems to be the current approach for responding to disinformation by public regulators across the world. Nobody is able to say with any degree of certainty what is actually going on. This is in no small part because, at present, public regulators don’t have the slightest idea how disinformation actually works in practice. We believe that there are very good reasons for the current state of affairs, which stem from a lack of verifiable data available to public institutions. If an election board or a media regulator wants to know what types of digital content are being shared in their jurisdiction, they have no effective mechanisms for finding this data or ensuring its veracity. While there are many other reasons why governments would want access to this kind of data, the phenomenon of disinformation provides a particularly salient example of the consequences of a lack of access to this data for ensuring free and fair elections and informed democratic participation. This chapter will provide an overview of the main aspects of the problems associated with basing public regulatory decisions on unverified data, before sketching out some ideas of what a solution might look like. In order to do this, the chapter develops the concept of auditing intermediaries. After discussing which problems the concept of auditing intermediaries is designed to solve, it then discusses some of the main challenges associated with access to data, potential misuse of intermediaries, and the general lack of standards for the provision of data by large online platforms. In conclusion, the chapter suggests that there is an urgent need for an auditing mechanism to ensure the accuracy of transparency data provided by large online platform providers about the content on their services. Transparency data that have been audited would be considered verified data in this context. Without such a transparency verification mechanism, existing public debate is based merely on a whim, and digital dominance is likely to only become more pronounced.
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Project objectives Radicalisation research leads to ethical and legal questions and issues. These issues need to be addressed in way that helps the project progress in ethically and legally acceptable manner. Description of Work The legal analysis in SAFIRE addressed questions such as which behavior associated with radicalisation is criminal behaviour. The ethical issues were addressed throughout the project in close cooperation between the ethicists and the researchers using a method called ethical parallel research. Results A legal analysis was made about criminal law and radicalisation. During the project lively discussions were held in the research team about ethical issues. An ethical justification for interventions in radicalisation processes has been written. With regard to research ethics: An indirect informed consent procedure for interviews with (former) radicals has been designed. Practical guidelines to prevent obtaining information that could lead to indirect identification of respondents were developed.
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