The time divide in cross-national perspective: The work week, gender and education in 17 countries

Prior empirical studies have found that American workers report longer hours than workers in other highly industrialized countries, and that the highly educated report the longest hours relative to other educational levels. This paper analyzes disparities in working hours by gender and education levels in 17 high- and middle-income countries in order to assess whether this finding holds cross-nationally. In contrast to many prior studies of working time, we use a measure of weekly rather than annual hours worked, which we argue provides a better window on the discretionary time available to individuals and households. We find that: 1) average weekly male hours in the United States do not appear exceptional, with averages exceeding 40 hours per week in both the U.S. and most western European countries; 2) U.S. women work longer hours than women in most other rich countries; 3) the within-country difference in average hours by education is not uniform, with higher-income countries more likely to show the U.S. pattern, and middle-income countries showing the reverse pattern, with the less educated reporting longer hours. We conclude by assessing some possible macro-level explanations for this variation, including per capita GDP, tax rates, unionization, and earnings inequality

The Future of Low Wage Work in Metropolitan America

Many researchers have analyzed the factors contributing to the rise of low wage work, explored the changing composition of the low wage labor force, examined the barriers to upward mobility faced by low wage workers, and suggested potential policy avenues for improving their living conditions (Appelbaum, Bernhard, and Murnane 2003, Shulman 2003, Bartik 2001, and Freeman and Gottschalk 1998). Given the changing circumstances of recent years, however, the time is ripe for -- and the field requires -- a fresh and comprehensive look at current labor market conditions and trends. In particular, the philanthropic community, labor market scholars, and those working in and with government to help low wage workers and new labor force entrants to move into better jobs all face the challenge of better understanding: 1) how the changing occupational structure of various industries in the national labor market influences wage patterns across industry/occupation cells or niches, 2) how the demographics of those who work in these cells are also changing, 3) how these trends play out across metropolitan places, and 4) which metropolitan areas and labor force segments show the most interesting trends, both in terms of upward mobility and in terms of driving the overall picture. This study provides information useful in addressing these questions for the period from 1980 to 2006, with a particular focus on the trends between 2000 and 2006

Scaling Methods for Matching Tasks in Turbocharged Engines

Specific turbocharger parameters are necessary to develop and match model based control strategies in the air path of a turbocharged engine. These parameters describe the turbocharger performance and are obtained from measurements on manufacturers’ standard test benches under steady state conditions and without taking into account the heat transfer between the components of the turbocharger or between the turbocharger and the surroundings. The latter falsifies the measured turbocharger efficiency which can be referred as “apparent efficiency”. The efficiency is a key parameter of the model based controls. Thus, the apparent efficiency increases the uncertainties (mismatching) and slows down the matching process considerably. Due to the mismatching, manufacturers’ parameters themselves need to be calibrated. The calibration occurs on the basis of on-board measurements and offline analyses. However, this calibration procedure is not axiomatic and the results remain typical for a certain turbocharger and engine combination. Hence, it is usually not possible to apply the results when the same turbocharger should be matched with another engine. A physically based scaling method has already been introduced in previous publications in order to obtain the “real” from the “apparent” efficiency, [1]. This work aims to show on the basis of a concrete example how the implementation of this method counteracts the mismatching without any further measurements. As a result, the matching process can be accelerated and enhanced. The reusability of the results leads to faster processes and lower costs

Controlled fabrication of single-walled carbon nanotube electrodes by electron-beam-induced oxidation

The fabrication of metallic single-walled carbon nanotube electrodes separated by gaps of typically 20nm width by electron-beam-induced oxidation is studied within an active device configuration. The tube conductance is measured continuously during the process. The experiment provides a statistical evaluation of gap sizes as well as the electron dose needed for gap formation. Also, the ability to precisely cut many carbon nanotubes in parallel is demonstrated.Comment: The following article has been submitted to Applied Physics Letters. After it is published, it will be found at http://apl.aip.or

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally

Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job

A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won’t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of ‘bounded automation’ to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial.

BACKGROUND: Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. METHODS: In this multicentre, open-label, randomised controlled trial, we recruited women aged 18-40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. FINDINGS: Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference -0·19%; 95% CI -0·34 to -0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). INTERPRETATION: Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. FUNDING: Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research