The COVID-19 crisis witnessed a major rise in investment in software for the digital organisation and rationalisation of work, while investment in robotics is continuously lagging behind expectations. This article argues that we can understand this development as the continuation of the rise of algorithmic management as a technological fix for profitability crises. Thus, in the face of falling wage rates and a structural overaccumulation of capital since the 1970s, algorithmic management has become an alternative to automation. The article reconstructs the history of algorithmic management in connection to economic crises. This allows for periodisation of the rise of algorithmic management from 'computer-integrated manufacturing' to remote work in four waves. In times of crisis, algorithmic management functions as a substitute for investment in 'tangible capital' such as robots. Structural economic forces thus interact with labour conflicts at the company level, shaping the rise of algorithmic management.
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http://dx.doi.org/10.1111/ntwe.12246 | DOI Listing |
BJOG
January 2025
Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Objective: To determine the diagnostic performance and clinical utility of the M4 prediction model and the NICE algorithm managing women with pregnancy of unknown location (PUL).
Design: The study has a superiority design regarding specificity for non-ectopic pregnancy for M4, given that the primary outcome of sensitivity for ectopic pregnancy (EP) is non-inferior in comparison with the NICE algorithm.
Setting: Emergency gynaecology units in Sweden.
Glob Chang Biol
January 2025
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
Terrestrial vegetation is a key component of the Earth system, regulating the exchange of carbon, water, and energy between land and atmosphere. Vegetation affects soil moisture dynamics by absorbing and transpiring soil water, thus modulating land-atmosphere interactions. Moreover, changes in vegetation structure (e.
View Article and Find Full Text PDFFront Neuroinform
January 2025
Department of Neurology, University Hospital Zurich, Zurich, Switzerland.
Purpose: The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management.
Participants: All centres ensure accuracy and completeness of the data.
Resusc Plus
January 2025
Department of Anesthesiology and Intensive Care Medicine, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Trauma care prioritizes life-threatening conditions using the ABCDE algorithm based on the principle "treat first what kills first". As for catastrophic hemorrhage, a leading preventable cause of death in trauma, modifications of this algorithm are necessary in specific cases. In cold climates, life-threatening hypothermia poses additional challenges.
View Article and Find Full Text PDFFront Oncol
January 2025
Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.
Methods: Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms.
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