Thursday, December 15, 2016

The ideological opposition to biological truth

Leftist-atheist-evolutionist professor Jerry Coyne writes:
One distressing characteristic of the Left, at least as far as science is concerned, is to let our ideology trump scientific data; that is, some of us ignore biological data when it’s inimical to our political preferences. This plays out in several ways: the insistence that race doesn’t exist (and before you accuse me of saying that races do exist, read about what I’ve written here before: the issue is complex), that there are no evolutionarily-based innate (e.g., genetically based) behavioral or psychological differences between ethnic groups, and that there are no such differences, either, between males and females within humans.

These claims are based not on biological data, but on ideological fears of the Left: if we admit of such differences, it could foster racism and sexism.  Thus. any group differences we do observe, whether they reside in psychology, physiology, or morphology, are to be explained on first principle as resulting from culture rather than genes. (I do of course recognize that culture can interact with genes to produce behaviors.) This ideological blinkering leads to the conclusion that when we see a difference in performance between groups and genders, the obvious explanation is culture and oppression, and the remedy is equal outcomes rather than equal opportunities. Yet in areas like most sports, where everyone agrees that males are on average larger and stronger than females, it’s clear that the behavioral differences (i.e., performance) result from biological differences that are surely based on evolution (see below). In sports like track and field or judo, nobody would think of making males compete with females. ...

Thus, to claim, as does P.Z. Myers in a new post, that higher testosterone levels in males have minimal influence on their aggressiveness compared to the effects of culture, is a claim based not on data—which show that he’s wrong—but on ideology.
Yes, Coyne is right. Leftists nearly always deny biological truth.

Update: Coyne responds to feminist criticism, and promises another response.

Update: Here is Coyne, The evolution of sexual dimorphism in humans: Part 2.

Update: Here is a comment:
Its high time we declared there are two major ideological enemies of evolutionary biology, and Right-wing/creationism is now less dangerous than the Leftist/feminist one.

The ways they will get their Leftist ideologies past science is by
1. pushing postmodernist poisons – truth does not exist, science is white male whatever, facts are oppressive.
2. calling scientists who are looking at evidence, data and facts from the point of view of truth and intellectual honesty as racist, sexist, bigoted etc to silence them.
3. insist any criticism of THEIR theories is hate, harassment and, ironically, “ideology”.

1 comment:

Matthew Cory said...

Singular Value Decomposition does produce less numerical error. It's amazing mathematicians have such big departments after computers were invented.

function [signals,PC,V] = pca1(data)
% PCA1: Perform PCA using covariance.
% data - MxN matrix of input data
% (M dimensions, N trials)
% signals - MxN matrix of projected data
% PC - each column is a PC
% V - Mx1 matrix of variances
[M,N] = size(data);
% subtract off the mean for each dimension
mn = mean(data,2);
data = data - repmat(mn,1,N);
% calculate the covariance matrix
covariance = 1 / (N-1) * data * data’;
% find the eigenvectors and eigenvalues
[PC, V] = eig(covariance);
% extract diagonal of matrix as vector
V = diag(V);
% sort the variances in decreasing order
[junk, rindices] = sort(-1*V);
V = V(rindices);
PC = PC(:,rindices);
% project the original data set
signals = PC’ * data;

function [signals,PC,V] = pca2(data)
% PCA2: Perform PCA using SVD.
% data - MxN matrix of input data
% (M dimensions, N trials)
% signals - MxN matrix of projected data
% PC - each column is a PC
% V - Mx1 matrix of variances
[M,N] = size(data);
% subtract off the mean for each dimension
mn = mean(data,2);
data = data - repmat(mn,1,N);
% construct the matrix Y
Y = data’ / sqrt(N-1);
% SVD does it all
[u,S,PC] = svd(Y);
% calculate the variances
S = diag(S);
V = S .* S;
% project the original data
signals = PC’ * data;

https://arxiv.org/pdf/1404.1100.pdf