Can you buy impact?

Yes you can. Only recently I came across an incredible example how a university has been pushing the own “impact”. It is the astonishing story of the King Abdulaziz University ( detected by Bits of DNA ) and goes back to an earlier Science article

In 2011 Yudhijit Bhattacharjee published an article in Science titled “Saudi Universities Offer Cash in Exchange for Academic Prestige” that describes how KAU is targeting highly cited professors for adjunct faculty positions.

Looks like the professional football league. If you do have a lot of money you can pay for whatever player you need for your club. There is, however, a big difference: As a player you have to show up on tht football field, as a scientist you just have to drop your name somewhere.

Wie mit Wissenschaftsleugnern umgehen?

Hier eine Reihe von Link Tipps (eine Zusammenfassung aus der Scientists for Future Mailing Liste mit eigenen Ergänzungen).

Fakten zählen leider hier wenig, dennoch die Sache ist nicht ganz hoffnungslos wenn man sich die Quellen ansieht, das ganze im Rollenspiel durchgeht, am besten einen Knopf im Ohr hat mit Supportern im Publikum.

Doubling time

I always had the impression that whenever a scientific work group is growing too quickly, it is difficult to keep ethical and scientific standards. Maybe that’s even true on macroscopic scale? The paper “Age, Aging and Age Structure in Science” by Robert Merton & Harriet Zuckerman summarizes in 1972 (on p 498 of Merton’s collected works “Sociology of Science”) that

the population of scientists, with a doubling time of about fifteen years, is far outrunning the acceleration rate of increase in the general population.

and he quotes Derek Price “Little science, big science” p 19 that we

will soon have two scientists for every man, woman, child and dog.

Organized ! Scepticism !! 2019 !!!

Here is the original 1942 text (“The Normative Structure of Science”) with an excerpt the 4th norm as found at

As we have seen in the preceding chapter, organized skepticism is variously interrelated with the other elements of the scientific ethos. It is both a methodological and an institutional mandate. The temporary suspension of judgment and the detached scrutiny of beliefs in terms of empirical and logical criteria have periodically involved science in conflict with other institutions. Science which asks questions of fact, including potentialities, concerning every aspect of nature and society may come into conflict with other attitudes toward these same data which have been crystallized and often ritualized by other institutions. The scientific investigator does not preserve the cleavage between the sacred and the profane, between that which requires uncritical respect and that which can be objectively analyzed.

As we have noted, this appears to be the source of revolts against the so-called intrusion of science into other spheres. Such resistance on the part of organized religion has become less significant as compared with that of economic and political groups. The opposition may exist quite apart from the introduction of specific scientific discoveries which appear to invalidate particular dogmas of church, economy, or state. It is rather a diffuse, frequently vague, apprehension that skepticism threatens the current distribution of power. Conflict becomes accentuated whenever science extends its research to new areas toward which there are institutionalized attitudes or whenever other institutions extend their control over science. In modern totalitarian society, anti-rationalism and the centralization of institutional control both serve to limit the scope provided for scientific activity.

Note also the footnote as the text was written in 1942 only. By 1948, the political leaders of Soviet Russia strengthened their emphasis on Russian nationalism and began to insist on the “national” character of science.
The Merton text contains perfect prediction of what is currently going on with the Scientists for Future movement – conflict becomes accentuated whenever science extends its research to new areas invalidating particular dogmas of church, economy, or state. If organized scepticism is an integral part of the scientific method (which I believe) conflicts are therefore preordained.

The science and art of scientific presentations

I always recommend Edward Tufte’s book The Visual Display of Quantitative Information” and what he says about data ink

  1. Above all else show data.
  2. Maximize the data-ink ratio.
  3. Erase non-data-ink.
  4. Erase redundant data-ink.
  5. Revise and edit.


nach Land

nach Einwohner

Territorial emissions

CDIAC: Boden, TA, Marland, G and Andres, RJ 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., USA. DOI: 10.3334/CDIAC/00001_V2017.
UNFCCC, 2017. National Inventory Submissions 2017. United Nations Framework Convention on Climate Change.
BP, 2017. Statistical Review of World Energy.

# R
co2pp <- read.csv("/Users/xxx/Desktop/export_20191116_1913.csv",sep = ";", quote = "\"'",)
visited <- colnames(co2)[-1]
ll.visited <- geocode(visited)
ll.visited[, "country"] <- c("Afghanistan","Albania","Algeria","Andorra","Angola","Anguilla","Antigua.and.Barbuda","Argentina","Armenia","Aruba","Australia","Austria","Azerbaijan","Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin","Bermuda","Bhutan","Bolivia","Bonaire..Saint.Eustatius.and.Saba","Bosnia.and.Herzegovina","Botswana","Brazil","British.Virgin.Islands","Brunei.Darussalam","Bulgaria","Burkina.Faso","Burundi","Cambodia","Cameroon","Canada","Cape.Verde","Cayman.Islands","Central.African.Republic","Chad","Chile","China","Colombia","Comoros","Congo","Cook.Islands","Costa.Rica","Côte.d.Ivoire","Croatia","Cuba","Curaçao","Cyprus","Czech.Republic","Democratic.Republic.of.the.Congo","Denmark","Djibouti","Dominica","Dominican.Republic","Ecuador","Egypt","El.Salvador","Equatorial.Guinea","Eritrea","Estonia","Ethiopia","Faeroe.Islands","Falkland.Islands..Malvinas.","Fiji","Finland","France","French.Guiana","French.Polynesia","Gabon","Gambia","Georgia","Germany","Ghana","Gibraltar","Greece","Greenland","Grenada","Guadeloupe","Guatemala","Guinea","Guinea.Bissau","Guyana","Haiti","Honduras","Hong.Kong","Hungary","Iceland","India","Indonesia","Iran","Iraq","Ireland","Israel","Italy","Jamaica","Japan","Jordan","Kazakhstan","Kenya","Kiribati","Kuwait","Kyrgyzstan","Laos","Latvia","Lebanon","Lesotho","Liberia","Libya","Liechtenstein","Lithuania","Luxembourg","Macao","Macedonia..Republic.of.","Madagascar","Malawi","Malaysia","Maldives","Mali","Malta","Marshall.Islands","Martinique","Mauritania","Mauritius","Mexico","Micronesia..Federated.States.of.","Moldova","Mongolia","Montenegro","Montserrat","Morocco","Mozambique","Myanmar","Namibia","Nauru","Nepal","Netherlands","New.Caledonia","New.Zealand","Nicaragua","Niger","Nigeria","Niue","North.Korea","Norway",NA,"Oman","Pakistan","Palau","Panama","Papua.New.Guinea","Paraguay","Peru","Philippines","Poland","Portugal","Qatar","Republic.of.South.Sudan","Réunion","Romania","Russian.Federation","Rwanda","Saint.Helena","Saint.Kitts.and.Nevis","Saint.Lucia","Saint.Pierre.and.Miquelon","Saint.Vincent.and.the.Grenadines","Samoa","Sao.Tome.and.Principe","Saudi.Arabia","Senegal","Serbia","Seychelles","Sierra.Leone","Singapore","Slovakia","Slovenia","Solomon.Islands","Somalia","South.Africa","South.Korea","Spain","Sri.Lanka","Sudan","Suriname","Swaziland","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","Timor.Leste","Togo","Tonga","Trinidad.and.Tobago","Tunisia","Turkey","Turkmenistan","Turks.and.Caicos.Islands","Tuvalu","Uganda","Ukraine","United.Arab.Emirates","United.Kingdom","United.States.of.America","Uruguay","Uzbekistan","Vanuatu","Venezuela","Vietnam","Wallis.and.Futuna.Islands","Western.Sahara","Yemen","Zambia","Zimbabwe")
ll.visited <-
cnd <- ll.visited$country %in% colnames(co2)
for(i in 1960:2017){
  ll.visited[cnd,"CO2"] <- as.vector(t(co2pp[i-1959,ll.visited$country[cnd]]))
  p <- ggplot() +
    geom_polygon(data = map_data("world"), aes(x=long, y=lat, group=group), fill="grey", alpha=0.2) +
    theme_void() + 
    xlim(-160,190) +
    ylim(-60,90) +
    geom_point( data=ll.visited, aes(x=lon, y=lat, size=CO2, fill=CO2), colour="black", alpha=.8, pch=21) +
    scale_color_brewer(type = 'div', palette = 'Spectral') +
    annotate("text", label=i, x=170, y=85, size=8.5) +
    scale_size(range = c(.1,14)) +
    guides( size = FALSE, fill = guide_colourbar(order = 1, title=expression('t CO'[2]*' per pers '), ) )
  fn <- paste("/Users/xxx/Desktop/tmp/",str_pad(i-1959, 3, pad = "0"),".png",sep="")
  ggsave(p, file=fn, width = 9, height = 4.5)
# ffmpeg -framerate 5 -i /Users/xxx/Desktop/tmp/%3d.png -r 5 -pix_fmt yuv420p -y /Users/wjst/Desktop/X/CO2.mp4



Nicht repräsentatives Sampling

Ob das Sinn macht? Krautreporter hat eine sehr positive Zusammenfassung von neueren Internet basierten Umfragemethoden

Eine nicht zufällig ausgewählte Stichprobe wird aber immer die zweitbeste Option bleiben. „Wenn man eine repräsentative, zufällige Stichprobe bekommen kann, wird jeder Wissenschaftler immer das nehmen”, sagt Gschwend. Das ist aber in der Regel viel teurer. Deshalb müsse man aus wirtschaftlichen Gründen die Nicht-zufällige-Stichprobe in der Wissenschaft vorantreiben und neue Verfahren mit Big Data weiterentwickeln.
Berechnet wird die Civey-Umfrage mit verschiedenen höheren statistischen Methoden, die Bayesianische Statistik, Riversampling, Poststratifizerung und Raking heißen, und die von deutschen Meinungsforschern noch recht selten verwendet werden.

Es gibt nur leider kaum eine Alternative zu einer repräsentativen oder einer totalen Befragung, auch bekannt als garbage in, garbage out Phänomen. Das bleibt so auch mit big data hype und Kalibration am Mikrozensus. Wenn Daten fehlen, dann fehlen sie. Einmal kann die Interpolation stimmen, aber beim entscheidenden nächsten Mal stimmt sie dann doch nicht. Dazu werden solche Umfragen durch immer ausgefeiltere Bots bedroht.

Civey und Opinary sind wohl mehr Marketing Methoden als Meinungsforschungsinstitute.

Was steht in dem zitierten wissenschaftliche Artikel?

After adjusting the Xbox responses via multilevel regression and poststratification, we obtain estimates which are in line with the forecasts from leading poll analysts.

Das Sample hier ist die Gamer Szene: 90% Männer, 10% Frauen, bei denen Stimmungsschwankungen in 3 Tagesabständen gemessen wurden.  Da Frauen bei der letzte Europawahl aber zu 24% Grüne wählten, aber Männer aber nur zu 18%, wäre der Forecast hier irrelevant, was heisst da schon ein Anstieg von plus 2% dieser Männer?
Und was ist mit Ereignissen kurz vor der Wahl, welche die Gamerszene nicht mitbekommen hat? Der Mobilisierung von Nichtwählern?
Zudem ist das amerikanische Wahlsystem nur eingeschränkt als Modell zu gebrauchen; die tatsächlichen 332 Wahlmänner wurde im übrigen mit der Vorsage 303 Wahlmänner deutlich verfehlt.