Steffen Mau, The Metric Society: On the Quantification of the Social (2019)

The Measurement of Social Value

By 'the quantification of the social', I mean that we are both complicit in and witness to a trend whereby social phenomena are increasingly measured, described and influenced by numbers. Interestingly, the German word for 'measure' (vermessen) has three distinct meanings, each of which will play a central role in this book. the first meaning denotes an action performed in order to make a quantitative statement about an object by comparing it with an established stan d ard (a dictionary definition gives: 'to ascertain the precise dimensions of something'). The second meaning - and here the German language provides a telling hint - is to 'mismeasure', or measure incorrectly . In other words, the process used to measure the object in question (systematically) produces mistakes glitches?? , and the results do not reflect reality. Thirdly, vermessen can be used in an adjectival sense to mean 'inappropriate' or even 'presumptious', which raises the critical question of where to draw the line between 'good' and 'bad' measurements. 
 
Taken together, these three meanings provide a triangular framework within which this book proposes to consider the issue  of  social  quantification.  Its  aspiration  extends  beyond  the  mechanics  of  measurement  itself,  however,  as  I  am  less  concerned with measuring techniques and errors, or the calibration of measuring technologies, than with the question of how  the  quantification  of  the  social  leads  to  new  forms  of  social  organization .  My  starting  observation  is  that  of  a  rapidly growing tendency to quantify the social world, accompanied by changes in the assignment of worth which are then translated  into  new  hierarchies.   Quantified  measurements  institutionalize  certain  ‘orders  of  worth’  which  provide  us  with benchmarks and justifications for viewing and evaluating  things  in  a  particular  way.  They  tell  us  which  activities,  achievements  or  qualities  have  a  high  ‘value’  and  which  do  not, thereby establishing specific normative principles (Boltanski & Chiapello 2005; Boltanski & Thévenot 2006). Through quantification,  classificatory  processes  of  definition,  evaluation and categorization are imposed in which the worth status of a person or thing is expressed in numbers. The use of new indicators, data and numerical notations to identify, describe and  evaluate  the  self  is  gradually  transforming  the  social  society into a metric one. Data make visible and define who we are, where we stand, how we are seen by others, and what our expectations should be.
 
The process of quantification is by no means a new social phenomenon. Its history dates back several millennia , to the early days of counting and the spread of mathematical knowledge. At first, the exploration of the world through numbers was the preserve of a small elite. Science, as a specific practice of  rationalization ,  has  of  course  shaped  and  developed  the  language  of  numbers  from  the  outset.  The  rise  of  modern  statehood  and  the  expansion  of  markets  and  capitalist  economics brought about a massive surge in the use of numbers in  everyday  economic,  political  and  social  practices.  The  availability  of  figures  in  the  form  of  official  statistics  made  possible techniques of governance which replaced the sacred with  objectivity  and  rationality.   On  the  markets,  the  spread  of ‘calculative practices’ (Vormbusch 2012) – as in bookkeeping and accounting, or the standardization of measurements and conversions – led to the emergence of a particular kind of economics and trade.    
  
In  the  following,  I  aim  to  show  that,  although  the  state  and markets were important starting points for the expansion of  calculative  practices,  the  language  of  numbers  has  since  become  universalized  to  a  degree  that  far  transcends  both  these  domains  and  that  of  science.  A  new  ‘quantitative mentality’  (Porter  1996:  118)  has  arisen,  with  profound  implications  for  our  social  environment.  This  mentality   accords  numbers  an  almost  a uratic *   pre-eminence   when  it  comes  to  identifying  social  phenomena,  and  is  now  leading  to an ever-widening reliance on all things numeric . Everything can, should or must be measured – nothing seems to be possible  without  numbers  any  more .  Social  semantics,  in  the  sense  of  how  society  observes  and  describes  itself,  draws  increasingly on the measurable side of the world, and of life in general. Of course, this shift is part of a long tradition of rationalization  efforts  aimed  at  organizing  social  and  economic life  according  to  the  principles  of  efficiency  and  predictability.  But  that’s  not  the  whole  story. 
 
 In  the  context  of  new forms of governance, a regime of control and evaluation has emerged which is based on the acquisition and processing of  data  and  whose  objective  is  performance  enhancement ,  capitalization and competition in very diverse domains; this regime  operates  via  targets,  performance  indicators kpi and  incentive systems which require growing volumes of data to be  produced  and  used  for  evaluation  purposes.  Qualitative  methods of assessment based on specifics are being replaced by  quantitative-style  evaluations  and  measurements.  To  put  it  another  way,  the  logic  of  optimization  and  performance  enhancement which neoliberalism has imposed on every conceivable  aspect  of  life  is  leading  to  a  straightforward  battle  for the best figures. Moreover, the more figures are produced, and  the  more  advanced  the  methods  of  data  collection  and  processing become, the easier it is to embed the standards for performance  and  self-improvement  within  the  social  fabric.  Now  that  data  have  evolved  into  the  reserve  currency  of  digitalized society, there are scarcely any natural boundaries left to halt this process. It is, in effect, infinite. ∞
  
    
What does quantification mean?
 
First, let us consider the question of what quantification actually means, and what it does . In general terms, quantification entails an act of translation : it expresses phenomena, characteristics or states of affairs in a general, abstract and universally accessible language – that of mathematics. This can be done  by  measurement  or  by  transforming  qualitative  judgements , insights and observations into numeric values. Quantification  reduces  a  complex  and  confusing  world  to  the  standardized  language  of  numbers,  in  which  there  are  clear  proportional relations between large and small (or more and less). Of course, there are different ways of talking about and understanding  observed  phenomena,  but  by  assigning  a  number to the thing observed, we take a step towards objectivizing it. Numbers, in short, are associated with precision, one-to-one  correspondence,  simplification,  verifiability  and  neutrality. As such, they are tailor-made for a prominent role in  societies  that  regard  themselves  as  rational  and  enlightened. Quantification often goes hand in hand with the existence of *transparent* and systematic operations for translating a social phenomenon into numbers. Key to the use of indicators  or  data  series  is  that  they  should  meet  certain  quality  criteria   and  be  largely  independent   of  whoever  generates  them. Results are expected to be determined by processes, not people  –  an  approach  that  echoes  scientific  practice.  At  the  same time, the quantification of social phenomena is a process of ‘ disembedding ’ which deliberately strips away local knowledge  and  the  context  of  social  practices  in  order  to  obtain  more abstract information that can be recombined and amalgamated with information from other sources.
transparent on some measurement but not in what the end result means
transparency as guise for neutrality
"research shows"
state transparency without going to the substance 
   
Without the presumption that statistical data are produced in a controlled manner and not merely arbitrary, they would be of little use. All numbers deployed in public discourse require a leap of faith – they have to be accepted as correct in order to exert their cold charisma . Numbers that no-one believes in have no value in social communication. For this reason, societies  go  to  great  lengths  to  place  self-quantification  data  on  a secure footing, for example by introducing comprehensive legislation on statistical affairs, creating statistics authorities, participating in international data-based monitoring systems or  developing  standardized  reporting  systems  in  virtually  every social subdomain. A country whose statistics don’t add up and which makes political decisions on the basis of incorrect or inadequate data can easily fall into disrepute among both its own population and the international community, as the  Greeks:) ancient greeks?   know  all  too  well.  Numbers  are  expected  to  be  accurate – whatever that may mean.
 
This is not to say that numbers are free from any kind of bias :  quite  the  contrary.  Ever  since  numbers  and  indicators  have featured in public and political discourse, they have also been  battled  over  by  interested  parties.  The  GDP  (Lepenies  2016), the unemployment rate, the public debt, the schwarze Null [balanced public finances] (Haffert 2016) – all these are contested  key  indicators ,  capable  of  triggering  public  anger,  economic downturns, political highs or even social crises, and politicians  are  therefore  well  advised  to  pay  close  attention  to  them,  from  agreeing  on  suitable  measuring  concepts  through deciding on presentation and publication frequency to discussing the political consequences of a given set of statistics.  covid situation.. regions and states tweaking data constantly.. The  politics  of  indicators  works  best  when,  in  the  perception  of  the  public ,  t he  theoretical  construct  and  the  indicator are seen as one . This would be the case, for instance, if  our  concept  of  intelligence  coincided  exactly  with  the  faculty  measured  by  intelligence  tests.*  Or  if  our  notion  of  human  development  matched  the  criteria  of  the  Human  Development Index, which takes into account only life expectancy, education and per capita GDP – a woefully inadequate measure from an empirical perspective.
-> https://www.dukeupress.edu/the-economization-of-life  
 
  
Numbers  offer  an  (often  very  convincing)  answer  to  our  need for objectivity, relevance and rationalization. Although they  abstract  from  concrete  social  contexts,  they  are  more  than mere mathematics. Underpinning them are value assignment  processes  that  give  numbers  their  meaning  in  the  first  place. Quantifications can thus be regarded as manifest forms of  worth  assignment,  which  is  why  it  is  not  only  the  act  of  quantifying  itself  that  matters,  but  how  it  is  done  and  by  whom.  ‘Statistics’,  according  to  Bettina  Heintz,  ‘claim  to  demonstrate  a  reality  which  exists  outside  of  them  and  is  rendered  visible  by  them.  In  truth,  however,  they  are  not  copies  of  a  pre-existing  reality,  but  selective  constructions  which  are  partly  responsible  for  creating  that  reality*.  The  objectivity of numbers is therefore not a fact, but an attribution ’* (2010: 170).

This view of quantification leads us inevitably to consider the  social  processes  involved  in  establishing  the  numerical  medium. Unlike price signals on markets, which serve to link supply  and  demand,  the  metrics  of  social  worth,  merit  or  performance  need  to  be  understood  primarily  as  social  and  cultural  premises.  All  numbers  contain  inherent  preconceptions as to what is relevant, valuable or authoritative (Espeland & Stevens 1998; Verran 2013). Data tell us how to look at things, thereby systematically excluding other perspectives . In other words, the use of numbers always represents a ‘particular  form  of  value  assignment’  (Vormbusch  2012:  24).  What  constitutes  a  good  education*,  what  efficient  government  means,  what  type  of  performance  counts  –  all  this  is  not only expressed, but socially instilled and institutionalized, by data. Numbers safeguard a particular order of worth and help  anchor  it  in  society  by  their  very  existence.  As  such,  there  is  a  close  correlation  between  value  estimation  in  the  context  of  quantification  and  esteem  in  the  sense  of  social  recognition ** .   ..."the outsourccing of human decision is, at once, in the insourcing of coded inequity."  From Race After Technology by Ruha Ben jamin

assessment
approached in qualitative manner than made quantitative.. request from the institution

** cf The goods of work (other than money!) Anca Gheaus and Lisa Herzog  
re work (in myanalytics) as collaboration / focus time and not wellbeing

movement that try to focus on progress, about how much formation of abilities happens
uni system based on an antiquated model of gaining credits

job market and how that influenced the re-organization of credits

abolish credit system > value / measure?

in THUAS formative assessment. feedback on the work, in a non-measured way
it's not what really counts in the end.
peer assessment / sharing work with classmates, discuss it etc.

in the end the grade makes these other forms irrelevant.

opportunity to question what type of innovation we want. 

assess a quality of a technology that is not yet there?!

what semantics do to discourse

if innovation is good we must innovating , a tool is good as it adheres to that notion of innovation

what are the responsibilities of the innovators within the society an d what role education can/should have in that  

different funding : not just venture capital, for example vaccines and other forms of 'innovation' comes from public funding

book: the smart enough city

what are the things that we want to leave unmeasured? even protect from measurment   __NOMEASURE__
what do we want to make more efficient?