Beyond books:High school librarians as champions of pupil inclusivity, autonomy, and reader development

School librarians can support and enhance pupils’ reading and personal development. However, there is very little research which has sought high school librarians’ perspectives of the diverse ways in which they do this. In this study, semi-structured interviews were carried out with 18 practitioners responsible for school library services from 18 geographically dispersed high schools in Scotland to gain in-depth insights into the practices they use to support pupil development. Inductive data-driven thematic analysis identified two themes. The first concerned reader development beyond reading for attainment. Within this theme it was identified that those with library responsibilities (a) cultivate a love of reading; (b) support diverse reading activities; (c) ensure book provision reflects the school community and (d) align reading with pupils’ contemporary lives. The second concerned pupils’ personal development. Within this theme it was identified that those with library responsibilities (a) support personal development; (b) expand pupils’ worldview through books; (c) support aspects of pupils’ lives; (d) provide support for minority pupils; (e) create a safe and social space and (f) support pupil autonomy. It is hoped that these findings will be of use to librarians and schools seeking to promote the vital role of libraries in supporting pupils beyond their academic attainment, as well as providing recommendations to researchers and practitioners seeking to support pupil inclusivity, autonomy, and individuality through school library services

COVID-19, ICT literacy, and Mental Health of University Students: A Three-Country Study

We ran a cross-national project examining the mental health of university students in Ghana, South Africa, and the United States against the backdrop of a surge in the digitalization of teaching at universities in these countries wrought by the COVID-19 pandemic

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images

We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce. In turn, to address these challenges, we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with curated CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION-2B data needed to train existing SD2 models, but obtains comparable quality. These results indicate that we have a sufficient number of CC images (~70 million) for training high-quality models. Our training recipe also implements a variety of optimizations that achieve ~3X training speed-ups, enabling rapid model iteration. We leverage this recipe to train several high-quality text-to-image models, which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on a human evaluation, despite being trained on our CC dataset that is significantly smaller than LAION and using synthetic captions for training. We release our models, data, and code at https://github.com/mosaicml/diffusion/blob/main/assets/common-canvas.m

2016-2017 Sanctuary Series - March 30, 2017

Watch video of the performance here.https://spiral.lynn.edu/sanctuary_series/1007/thumbnail.jp

Young people's data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom

Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young people's preferences for how their data are governed and quantifies potential participants' willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16-24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the "right people" access their data. Throughout the study, we recognized young people's commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research