The expertocratic shortcut

Lafont rightly criticizes the expertocratic shortcut, i.e. the expectation that citizens blindly defer to experts. This shortcut is based on the assumptions that citizens are generally politically ignorant. I will argue that it is necessary to address not only the political ignorance of citizens, but also that of politicians, scientific experts and entrepreneurs. Just like politicians, scientific experts often show political ignorance towards citizens. This is the case if they do not consider the perspective of citizens in politically charged research. Unlike Lafont, I believe that if citizens are expected to blindly defer to politicians, there is no expertocratic shortcut, but a form of authoritarianism that fosters populism. Lafont overlooks an expertocratic shortcut: scientific experts who expect politicians to blindly defer to them, i.e. to accept the agenda they set. It is noteworthy that neither Lafont nor her opponents defending an expertocratic shortcut explicitly discuss the tension between capitalism and democracy. They should do so in order to explain which citizens are ignorant and bypassed, and which are not. The socio-economic inequalities inherent in capitalism correspond to the degree of political interest and participation of citizens. Entrepreneurs are often not only politically ignorant towards citizens, but also towards politicians and scientific experts. Their political ignorance is due to the fact that they are most often in a dominant position where politicians, scientific experts and citizens depend on them. This can be traced back to what I call the neoliberal shortcut: the expectation that actors blindly defer to the markets

Linkse sprakeloosheid. Introductie.

De roep om een nieuw beschavingsoffensief klinkt de afgelopenjaren steeds luider. Kern daarvan is dat veel burgers opnieuwmoeten worden opgevoed tot sociaal, fatsoenlijk, beleefd gedrag, endat alleen straffen van onwenselijk gedrag daarbij niet volstaat.Deze roep om een beschavingsoffensief komt voornamelijk vanconservatief rechts. Opmerkelijk is dat het verzet tegen deconservatief-rechtse 'tegeltjeswijsheden' ook vooral vanuit derechterkant komt

Selecting sites for afforestation to minimize sediment loss from a river basin: computational complexity of single and multiple flow direction methods in raster databases

To minimize the sediment flowing to the outlet of a river catchment with minimal effort or cost, the best areas to perform a certain intervention, e.g., afforestation, must be selected. CAMF is a method that performs this selection process iteratively in a raster geo-database. The original version of CAMF uses a Single Flow Direction (SFD) algorithm to simulate the flow paths. Although SFD is often used in studies related to hydrological applications, it may fail to reflect the nature of flow transport, depending on the topography. This paper describes and analyzes the integration of Multiple Flow Direction (MFD) methods in CAMF, in order to provide more accurate flow simulations in areas with specific topographic characteristics. We compare the computational complexity of CAMF-SFD and CAMF-MFD using two methods: FD8 and D∞, and we discuss the scalability w.r.t. the number of cells involved. We evaluate the behavior of these three variants for sediment yield minimization by afforestation in two catchments with different properties

Fraction dead vs time from triage, by latent class.

Fraction dead vs time from triage, by latent class.</p

Triage data and laboratory results.

BackgroundCOVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden.MethodsThis was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers’ Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA).FindingsAmong 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p ConclusionEven though clinical and laboratory characteristics differed significantly between the two waves, mortality remained constant. According to LCA, the cardiovascular, diabetes, and CKD phenotypes had the highest death probability.</div

Reported complications.

BackgroundCOVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden.MethodsThis was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers’ Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA).FindingsAmong 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p ConclusionEven though clinical and laboratory characteristics differed significantly between the two waves, mortality remained constant. According to LCA, the cardiovascular, diabetes, and CKD phenotypes had the highest death probability.</div

Fraction dead vs. time from triage, by wave.

BackgroundCOVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden.MethodsThis was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers’ Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA).FindingsAmong 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p ConclusionEven though clinical and laboratory characteristics differed significantly between the two waves, mortality remained constant. According to LCA, the cardiovascular, diabetes, and CKD phenotypes had the highest death probability.</div

Demographic, lifestyle, and clinical characteristics of the sample.

Demographic, lifestyle, and clinical characteristics of the sample.</p