Читать книгу Quantifying Human Resources - Clotilde Coron - Страница 10

IQ tests are probably the most widely known tests of human intellectual ability for the general public. There are actually two definitions of IQ: intellectual development speed index (IQ-Stern) or group positioning index (IQWechsler). IQ-Stern depends on the age of the individual and measures the intellectual development of children. The IQ-Wechsler, defined in the late 1930s, is not a quotient, as its name suggests, but a device for calibrating individuals’ scores on an intellectual test. For example, an IQ of 130 corresponds to a 98 percentile (98% of the population scores below 130), while an IQ of 115 corresponds to the third quartile (75% of the population scores below 115). There are many debates about IQ tests. In particular, its opponents point out that tests measure only one form of intelligence, or that test results may depend to a large extent on educational inequalities, which makes them of little use in formulating educational policies.

Less well known to the general public, Spearman’s theory of the g factor is based on the observation that the results of the same individual on different intelligence tests are strongly correlated with each other, and infers that there is a common factor of cognitive ability. The challenge is therefore to measure this common factor. Multiple models were thus proposed during the 20th Century.

The second stance takes the opposite approach to this one by demonstrating its limits. Several arguments are put forward to this effect. The first challenges the notion of objectivity by highlighting the many evaluation biases faced by the psychotechnical approach (Gould 1997). These evaluation biases constitute a form of indirect discrimination: an apparently neutral test actually disadvantages some populations (women and ethnic minorities, for example). For example, intelligence tests conducted in the United States at the beginning of the 20th Century produced higher average scores for whites than blacks (Huteau and Lautrey 2006). These differences could be interpreted as hereditary differences, and could have contributed to racist theories and discourse, whereas in fact they illustrated the importance of environmental factors (such as school attendance) for test success, and thus showed that the test did not measure intelligence independently from a social context, but rather intelligence largely acquired in a social context (Marchal 2015). Moreover, this type of test, like craniometry, is based on the idea that human intelligence can be reduced to a measurement, subsequently allowing us to classify individuals on a one-dimensional scale, which is an unproven assumption (Gould 1997).

The second argument criticizes the decontextualization of psychotechnical measures, whereas many individual behaviors and motivations are closely linked to their context (e.g., work). This argument can be found in several theoretical currents. Thus, sociologists, ergonomists and some occupational psychologists argue that the measurement of intelligence is all the more impossible to decontextualize since it is also distributed outside the limits of the individual: it depends strongly on the people and tools used by the individual (Marchal 2015). However, as Marchal (2015) points out, work activities are “situated”, i.e. it is difficult to extract the activity from the context (professional, relational) in which it is embedded. This criticism is all the more valid for tests aimed at measuring a form of generic intelligence or performance, which is supposed to guarantee superior performance in specific areas. The g factor theory (Box I.1) is an instructive example of this decontextualized generalization, since it claims to measure a generic ability that would guarantee better performance in specific work activities. In practice, the same person, therefore with the same measure of g factor, may prove to be highly, or on the contrary, not very efficient depending on the work context in which he or she is placed.

The third argument questions the ethical legitimacy of the measurement of the individual and highlights in particular the possible excesses of this approach. Thus, the racist or sexist abuses to which craniometry or intelligence tests have given rise to are pointed out to illustrate the dangers of measuring intelligence (Gould 1997). In a more precise field of evaluation, many studies have highlighted the harms of quantified, standardized evaluation of individuals. In particular, Vidaillet (2013) denounces three of them. The first harm of quantified evaluation is that it contributes to changing people’s behavior, and not always in the desired direction. A known example of such a perverse effect is that of teachers who, being scored on the basis of their students’ scores on a test in the form of MCQs, are encouraged either to concentrate all their teaching on learning the skills necessary to succeed on the test, to the detriment of other, often fundamental skills, or to cheat to help their students when taking the test (Levitt and Dubner 2005). The second disadvantage is that it may harm the working environment by accentuating individual differences in treatment and thus increase competition and envy. The third harm is that it substitutes an extrinsic motivation (“I do my job well because I want a positive evaluation”) for an intrinsic motivation (“I do my job well because I like it and I am interested”). However, extrinsic motivation may reduce the interest of work for the person and therefore the intrinsic motivation: the two motivations are substitutable and not complementary.

Finally, the fourth argument emphasizes that, unlike objects and things, human beings can react and interact with the quantification applied to them. Thus, Hacking (2001, 2005) studies classification processes and more particularly human classifications, i.e. those that concern human beings: obesity, autism, poverty etc. He then refers to “interactive classification”, in the sense that the human being can be affected and even transformed by being classified in a category, which can sometimes lead to changes in category. Thus, a person who is entering the “obese” category after gaining weight may, due to this simple classification, want to lose weight and may therefore leave the category. This is what Hacking (2001, p. 9) calls the “loop effect of human specifications”. He recommends that the four elements underlying human classification processes (Hacking 2005) be studied together: classification and its criteria, classified people and behaviors, institutions that create or use classifications, and knowledge about classes and classified people (science, popular belief, etc.). Therefore, the possibility of quantifying human beings in a neutral way comes up against these interaction effects.

Finally, the confrontation between these two stances clearly shows the questions raised by the use of quantification when it comes to humans, and in HR notably: is it possible to measure everything when it comes to human beings? At what price? What are the implications, risks and benefits of quantification? Can we do without quantification?