Picture Books with Female Heroes

During the past several years, articles have been appearing in the women\u27s magazines and in educational journals which have told of the meager or negative portrayal of females to be found in books for the youngest children. During 1973 and 1974, in preparation for a workshop concerning female roles in literature, I searched libraries for books which feature females. In addition, I contacted 21 major publishers and asked for such books. Twelve publishers kindly allowed me to examine all the books they considered to be appropriate—that is, books which would have female protagonists. After my visits to countless libraries and bookstores and the arrival of the publishers\u27 books, it turned out that I had fewer than 100 books to choose from. Some of the publishers had sent books from the mid-sixties and called them recent. Others had sent books in which females had auxiliary roles or shared lead roles with males. Some even sent books in which the females were animals or machines! Thus, it seems, publishers are still very hesitant to publish books with females as main characters, probably basing their resistance upon the studies which have shown that boys are unwilling to read so-called girls\u27 books, while girls are quite willing to read the so-called boys\u27 books

Arts Entrepreneurship through Strategic Collaboration in Korean Classical Music

Arts Entrepreneurship is a comparatively new concept in arts management however, it is inevitable for the arts, especially classical music to adapt the concept for its survival. This article investigates how arts entrepreneurship is executed through strategic collaboration in three different cases of classical music organizations in Seoul, Korea: Yellow Lounge Seoul, Ensemble Ditto and The New Baroque Company. By providing vivid examples of how to apply arts entrepreneurship in classical music products, it will better help to understand the concept. The study conducted a focused group interview (FGI) with concert with classical music marketing specialists and their strategic collaborators. The framework of entrepreneurial orientation (EO) was applied to analyze identify arts entrepreneurship in each organization. The entrepreneurial approaches of these organizations are identified by how their entrepreneurial orientation (EO) is executed in their innovativeness, risk-taking, and proactiveness. The results of this empirical study are demonstrated in three aspects: 1) Strategic collaboration with an unconventional partner resulted in realization of entrepreneurial orientation. 2) Entrepreneurship through strategic collaboration resulted in reducing production costs, sourcing new funds, increasing the audience base and performance opportunities 3) Arts entrepreneurship was designed to maintain the core value that the quality of music would not be compromised or altered

Comparing Code Explanations Created by Students and Large Language Models

Reasoning about code and explaining its purpose are fundamental skills for computer scientists. There has been extensive research in the field of computing education on the relationship between a student's ability to explain code and other skills such as writing and tracing code. In particular, the ability to describe at a high-level of abstraction how code will behave over all possible inputs correlates strongly with code writing skills. However, developing the expertise to comprehend and explain code accurately and succinctly is a challenge for many students. Existing pedagogical approaches that scaffold the ability to explain code, such as producing exemplar code explanations on demand, do not currently scale well to large classrooms. The recent emergence of powerful large language models (LLMs) may offer a solution. In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code. To evaluate LLM-created explanations, we compare them with explanations created by students in a large course ($n \approx 1000$) with respect to accuracy, understandability and length. We find that LLM-created explanations, which can be produced automatically on demand, are rated as being significantly easier to understand and more accurate summaries of code than student-created explanations. We discuss the significance of this finding, and suggest how such models can be incorporated into introductory programming education.Comment: 8 pages, 3 figures. To be published in Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V.

Automatically Generating CS Learning Materials with Large Language Models

Recent breakthroughs in Large Language Models (LLMs), such as GPT-3 and Codex, now enable software developers to generate code based on a natural language prompt. Within computer science education, researchers are exploring the potential for LLMs to generate code explanations and programming assignments using carefully crafted prompts. These advances may enable students to interact with code in new ways while helping instructors scale their learning materials. However, LLMs also introduce new implications for academic integrity, curriculum design, and software engineering careers. This workshop will demonstrate the capabilities of LLMs to help attendees evaluate whether and how LLMs might be integrated into their pedagogy and research. We will also engage attendees in brainstorming to consider how LLMs will impact our field.Comment: In Proceedings of the 54th ACM Technical Symposium on Computing Science Educatio

Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book

Advances in natural language processing have resulted in large language models (LLMs) that are capable of generating understandable and sensible written text. Recent versions of these models, such as OpenAI Codex and GPT-3, can generate code and code explanations. However, it is unclear whether and how students might engage with such explanations. In this paper, we report on our experiences generating multiple code explanation types using LLMs and integrating them into an interactive e-book on web software development. We modified the e-book to make LLM-generated code explanations accessible through buttons next to code snippets in the materials, which allowed us to track the use of the explanations as well as to ask for feedback on their utility. Three different types of explanations were available for students for each explainable code snippet; a line-by-line explanation, a list of important concepts, and a high-level summary of the code. Our preliminary results show that all varieties of explanations were viewed by students and that the majority of students perceived the code explanations as helpful to them. However, student engagement appeared to vary by code snippet complexity, explanation type, and code snippet length. Drawing on our experiences, we discuss future directions for integrating explanations generated by LLMs into existing computer science classrooms

Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models

Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle. In certain conditions, buggy code may even exhibit correct behavior -- in other cases, the issue might be about how a problem statement has been interpreted. Such errors can be hard to spot when reading the code, and they can also at times be missed by automated tests. There is great educational potential in automatically detecting logic errors, especially when paired with suitable feedback for novices. Large language models (LLMs) have recently demonstrated surprising performance for a range of computing tasks, including generating and explaining code. These capabilities are closely linked to code syntax, which aligns with the next token prediction behavior of LLMs. On the other hand, logic errors relate to the runtime performance of code and thus may not be as well suited to analysis by LLMs. To explore this, we investigate the performance of two popular LLMs, GPT-3 and GPT-4, for detecting and providing a novice-friendly explanation of logic errors. We compare LLM performance with a large cohort of introductory computing students $(n=964)$ solving the same error detection task. Through a mixed-methods analysis of student and model responses, we observe significant improvement in logic error identification between the previous and current generation of LLMs, and find that both LLM generations significantly outperform students. We outline how such models could be integrated into computing education tools, and discuss their potential for supporting students when learning programming

Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits

Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma

Idarucizumab for Dabigatran Reversal - Full Cohort Analysis.

BACKGROUND: Idarucizumab, a monoclonal antibody fragment, was developed to reverse the anticoagulant effect of dabigatran. METHODS: We performed a multicenter, prospective, open-label study to determine whether 5 g of intravenous idarucizumab would be able to reverse the anticoagulant effect of dabigatran in patients who had uncontrolled bleeding (group A) or were about to undergo an urgent procedure (group B). The primary end point was the maximum percentage reversal of the anticoagulant effect of dabigatran within 4 hours after the administration of idarucizumab, on the basis of the diluted thrombin time or ecarin clotting time. Secondary end points included the restoration of hemostasis and safety measures. RESULTS: A total of 503 patients were enrolled: 301 in group A, and 202 in group B. The median maximum percentage reversal of dabigatran was 100% (95% confidence interval, 100 to 100), on the basis of either the diluted thrombin time or the ecarin clotting time. In group A, 137 patients (45.5%) presented with gastrointestinal bleeding and 98 (32.6%) presented with intracranial hemorrhage; among the patients who could be assessed, the median time to the cessation of bleeding was 2.5 hours. In group B, the median time to the initiation of the intended procedure was 1.6 hours; periprocedural hemostasis was assessed as normal in 93.4% of the patients, mildly abnormal in 5.1%, and moderately abnormal in 1.5%. At 90 days, thrombotic events had occurred in 6.3% of the patients in group A and in 7.4% in group B, and the mortality rate was 18.8% and 18.9%, respectively. There were no serious adverse safety signals. CONCLUSIONS: In emergency situations, idarucizumab rapidly, durably, and safely reversed the anticoagulant effect of dabigatran. (Funded by Boehringer Ingelheim; RE-VERSE AD ClinicalTrials.gov number, NCT02104947 .)

The effectiveness of smoking cessation interventions for socio-economically disadvantaged women: A systematic review and meta-analysis

IntroductionThis systematic review and meta-analysis assessed the effectiveness of smoking cessation interventions among women smokers in low socio-economic status (SES) groups or women living in disadvantaged areas who are historically underserved by smoking cessation services.MethodsA systematic literature search was conducted using MEDLINE (OVID), EMBASE, Cochrane, CINAHL, PsychINFO and Web of Science databases. Eligibility criteria included randomised controlled trials of any smoking cessation intervention among women in low SES groups or living in socio-economically disadvantaged areas. A random effects meta-analysis assessed effectiveness of interventions on smoking cessation. Risk of bias was assessed with the Cochrane Risk of Bias tool. The GRADE approach established certainty of evidence.ResultsA total of 396 studies were screened for eligibility and 11 (6153 female participants) were included. Seven studies targeted women-only. 5/11 tested a form of face-to-face support. A pooled effect size was estimated in 10/11 studies. At end of treatment, two-thirds more low SES women who received a smoking cessation intervention were more likely to stop smoking than women in control groups (risk ratio (RR) 1.68, 95% CI 1.36–2.08, I2= 34%). The effect was reduced but remained significant when longest available follow-up periods were pooled (RR 1.23, 95% CI 1.04–1.48, I2 = 0%). There was moderate-to-high risk of bias in most studies. Certainty of evidence was low.ConclusionsBehavioural and behavioural + pharmacotherapy interventions for smoking cessation targeting women in low SES groups or women living in areas of disadvantage were effective in the short term. However, longer follow-up periods indicated reduced effectiveness. Future studies to explore ways to prevent smoking relapse in this population are needed.Systematic review registrationPROSPERO: CRD4201913016