Originally posted by Nereid
Originally posted by hitssquad quoting from a CB publication:
How are the race categories used in Census 2000 defined?
“White” refers to people having origins in any of the original peoples of Europe, the Middle East, or North Africa... etc
Actually, that's only how the OMB/CB wrote it; how each of the >250 million people who put multiple "X" in squares
It was
6.8 million people who put multiple "X" in squares in Question 6 (Q6).
--
The overwhelming majority of
the U.S. population reported
only one race.
In Census 2000, nearly 98 percent
of all respondents reported only
one race (see Table 1). The largest
group reported White alone, accounting
for 75 percent of all
people living in the United States.
The Black or African American
alone population represented
12 percent of the total. [/color]
--
(p3 of 11)
http://www.census.gov/prod/2001pubs/c2kbr01-1.pdf
--
Only 2.4 percent of all
respondents reported two or
more races.
The Two or more races category
represents all respondents who reported
more than one race. The six
race categories of Census 2000 can
be put together in 57 possible combinations
of two, three, four, five, or
six races (see Table 2). Less than
3 percent of the total population reported
more than one race. Of the
6.8 million respondents who reported
two or more races, 93 percent
reported exactly two. [/color]
--
(p5 of 11)
http://www.census.gov/prod/2001pubs/c2kbr01-1.pdf
is known only to those folk; they could be lying, ignorant of their "origins", feeling that "it's none of your *** business", etc
This lack of discrete access to quantum force vector datum was acknowledged https://www.physicsforums.com/showthread.php?s=&postid=150837&highlight=answer#post150837 (02-22-2004) by the present author who noted in the same instance that what is important from a statistical worldview is statistical assessment of consistency, not discrete knowledge:
--
Nereid wrote:
with 'race' ... self-identification hardly constitutes a reliable basis,
As far as a statistical viewpoint is concerned, statistical methods quantify reliability. Statistical methods applied to instances of self-identification of racial categories return quantifications of reliability in those instances.
Unreliability in an instance of racial self-identification would imply, over the lifetimes of a substantial portion of individuals in the populations under study, systematic inconsistency in the answering of the institutionally-posed race-category question. E.g., a given unreliably-self-identifying individual may claim to be black one year, white the next, then asian, then American Indian, etc. [/color]
--
https://www.physicsforums.com/showthread.php?s=&postid=150837&highlight=answer#post150837
In fact, from studies which Jensen himself cites, we know that almost all those who put an "X" in the "Black" square should (if they only knew) have also put an "X" in the "White" square,
"Black" does not necessarily mean 100% sub-Saharan negroid African. For whatever reason, or reasons, there seems to be a population in the United States that relatively consistently self-identifies as black. This population has been found to possesses largely African genetic markers mixed with a smaller portion of European genetic markers, both within the population as a whole and within individual members of the population.
A recent estimate puts the average percetage of Europeans markers in individual self-identified blacks at 17%:
--
Shriver's project is not complete, but with data from 25 sites already in, he is coming up with 17-18 percent white ancestry among African-Americans. That's the equivalent of 106 of those 128 of your ancestors from seven generations ago having been Africans and 22 Europeans.
According to Shriver, only about 10 percent of African-Americans are over 50 percent white.
This genetic database is restricted to adults. Black-white married couples quadrupled in number between the 1960 Census and 1990 Census, so the admixture rates among children are no doubt higher than among adults. [/color]
--
http://groups.yahoo.com/group/e-l/message/9213
Originally posted by Nereid
hitssquad again, quoting Jensen
There is considerable variation within these broad groups, of course, and there are many other derivative or blended groups that could be called races...
...the criteria for all of the classifications are genetically based.
Translation: because there's a lot of variation, there's no clear, consistent means to define a 'human race', and (modern extension) studies of human genetic variation support the conclusion that 'race' is, biologically, for humans, a largely arbitrary choice.
--
The important point is that the average difference between individuals
within a group is less than the average difference
between groups on the relevant physical characteristics, whether at the molecular level or the gross physical level of measurement. [/color]
--
Intelligence, Race, and Genetics. p117.
https://www.amazon.com/gp/product/081334008X/?tag=pfamazon01-20
we have a test-based construct
All constructs require tests and statistical analyses of those tests to be revealed. The tests themselves do not constitute bases of constructs.
--
Spearman argued that a collection of items as found in Binet's test "works" only because g enters into any and every mental task... In Spearman's words:
- This means that, for the purpose of indicating the amount of g possessed by a person, any test will do just as well as any other, provided only that its correlation with g is equally high. With this proviso, the most ridiculous "stunts" will measure the self-same g as will the highest exploits of logic or flights of imagination... And here, it should be noticed, we come at last upon the secret of why all the current tests of "general intelligence" show high correlations with one another, as also with g itself. The reason lies, not in the theories inspiring these tests (which theories have been most confused), nor in any uniformity of construction (for this has often been wildly heterogeneous), but wholly and solely in the above shown "indifference of the indicator." (1927, pp. 197-198)
[/color]--
The g Factor. (p33)
http://www.questia.com/PM.qst?a=o&d=24373874
--
Factor. The word "factor" has a number of dictionary definitions, but the term as used here has a very restricted, specialized meaning. A factor is a hypothetical variable that "underlies" an observed or measured variable. Thus a factor is also referred to as a latent variable. It is best thought of initially in terms of the mathematical operations by which we identify and measure it.
Although a factor is identifiable and quantifiable, it is not directly observable. It is not a tangible "thing" or an observable event. So we have to be especially careful in talking about factors, lest someone think we believe that we are talking about "things" rather than hypothetical and mathematical constructs. But one can say the very same thing about the many constructs used in the physical sciences (gravitation, magnetism, heat, valence, and potential energy, to name a few). They are all constructs. This does not imply, however, that scientists cannot inquire about the relationship of a clearly defined construct to other phenomena or try to fathom its causal nature. Nor is a construct "unreal" or "chimerical" or less important than some directly observable action or tangible object. Certainly the force of gravity (a hypothetical construct) has more widespread importance than the particular chair I am sitting in at the moment, and is every bit as real. A lot of pointless arguments can be avoided by consistently maintaining a clear distinction between the purely mathematical definition, identification, or measurement of factors, on the one hand, and theories about their causal nature, on the other. [/color]
--
The g Factor. (pp55-56)
http://www.questia.com/PM.qst?a=o&d=24373874
Discrete knowledge of the origins of statistical data is not necessary for that data to be used to produce consistently effective technology. Discrete knowledge of any possible biological origins of g is not necessary for the g construct to be effectively used to produce consistently effective technology.
- correlated with a social construct (with well-known political and social baggage)
Americans self-identify with racial groups with measurable and quantifiable consistency.
being used to draw 'scientific' conclusions
What they are used for is the production of consistently effective technology. Whether or not it reaches some arbitrary person's standard of
scientific is irrelevant to the effectiveness of the technology produced.
about the innate, biological capabilities of those social groups.
In a statistical worldview (field-
independent;
process oriented), things do not have innate characteristics; rather, any characteristics which might be observed or recorded are necessarily situation specific.
The reverse is true of a discrete worldview (field-
dependent;
outcome oriented).
[Edit: formatting adjusted][/color]
-Chris
[b(] Nereid did not say that; in fact, in several posts - including at least one in reply to SelfAdjoint - Nereid stated that the g-construct may be relatively unremarkable, but that there seemed to be big problems with extending it down (e.g. genes) and up (e.g. national wealth).