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Physical Sciences and Mathematics Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Pars Playwork: Considering Who We Are Becoming And Why, Janine Dodge
Pars Playwork: Considering Who We Are Becoming And Why, Janine Dodge
International Journal of Playwork Practice
The PARS model of playwork practice was developed by Dr Shelly Newstead from research into the origins of playwork from the adventure playgrounds set up in the UK just after the Second World War. This article considers why and how this theoretical model of playwork as a form of professional practice is now being applied and developed by practitioners in Brazil, contributing to the creation of an international ‘community of practice’ (Wenger et al, 2002). It describes how PARS’ underpinning philosophy and model of practice provide a strong foundation that values playwork and supports the development of a shared language …
The Play Workforce In Wales – Perceptions From Local Authority Play Sufficiency Lead Officers, Pete King, Justine Howard Dr
The Play Workforce In Wales – Perceptions From Local Authority Play Sufficiency Lead Officers, Pete King, Justine Howard Dr
International Journal of Playwork Practice
As part of the Welsh Play Workforce Study, seven lead local authority officers responsible for facilitating the three-year Play Sufficiency Assessment (PSA) were interviewed in respect of Matter G: Securing and developing the play workforce development. Thematic analysis constructed three themes from the findings: play profile, collaboration and funding. Although each lead officer was passionate about the importance of play, their play profile differed concerning their play and playwork experience, knowledge and qualifications. The study indicates the importance of collaborative and partnership working both within and external to the local authority, especially with the ever-changing play-related policy and potential funding …
Some Advice For Psychologists Who Want To Work With Computer Scientists On Big Data, Cornelius J. König, Andrew M. Demetriou, Philipp Glock, Annemarie M. F. Hiemstra, Dragos Iliescu, Camelia Ionescu, Markus Langer, Cynthia C. S. Liem, Anja Linnenbürger, Rudolf Siegel, Ilias Vartholomaios
Some Advice For Psychologists Who Want To Work With Computer Scientists On Big Data, Cornelius J. König, Andrew M. Demetriou, Philipp Glock, Annemarie M. F. Hiemstra, Dragos Iliescu, Camelia Ionescu, Markus Langer, Cynthia C. S. Liem, Anja Linnenbürger, Rudolf Siegel, Ilias Vartholomaios
Personnel Assessment and Decisions
This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, …
“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak
“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak
Personnel Assessment and Decisions
Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; …