I gave two counterexamples of why.

The examples you gave aren't counterexamples.  See below...

On Wed, May 19, 2010 at 7:06 PM, Darren Dale wrote:
On Wed, May 19, 2010 at 4:19 PM,  <josef.pktd@gmail.com> wrote:
> On Wed, May 19, 2010 at 4:08 PM, Darren Dale <dsdale24@gmail.com> wrote:
>> I have a question about creation of numpy arrays from a list of
>> objects, which bears on the Quantities project and also on masked
>> arrays:
>>
>>>>> import quantities as pq
>>>>> import numpy as np
>>>>> a, b = 2*pq.m,1*pq.s
>>>>> np.array([a, b])
>> array([ 12.,   1.])
>>
>> Why doesn't that create an object array? Similarly:
>>

Consider the use case of a person creating a 1-D numpy array:
> np.array([12.0, 1.0])
array([ 12.,  1.])

How is python supposed to tell the difference between
> np.array([a, b])
and
> np.array([12.0, 1.0])
?

It can't, and there are plenty of times when one wants to explicitly initialize a small numpy array with a few discrete variables.

>>>>> m = np.ma.array([1], mask=[True])
>>>>> m
>> masked_array(data = [--],
>>             mask = [ True],
>>       fill_value = 999999)
>>
>>>>> np.array([m])
>> array([[1]])
>>

Again, this is expected behavior.  Numpy saw an array of an array, therefore, it produced a 2-D array. Consider the following:

> np.array([[12, 4, 1], [32, 51, 9]])

I, as a user, expect numpy to create a 2-D array (2 rows, 3 columns) from that array of arrays.

>> This has broader implications than just creating arrays, for example:
>>
>>>>> np.sum([m, m])
>> 2
>>>>> np.sum([a, b])
>> 13.0
>>

If you wanted sums from each object, there are some better (i.e., more clear) ways to go about it.  If you have a predetermined number of numpy-compatible objects, say a, b, c, then you can explicitly call the sum for each one:
> a_sum = np.sum(a)
> b_sum = np.sum(b)
> c_sum = np.sum(c)

Which I think communicates the programmer's intention better than (for a numpy array, x, composed of a, b, c):
> object_sums = np.sum(x)       # <--- As a numpy user, I would expect a scalar out of this, not an array

If you have an arbitrary number of objects (which is what I suspect you have), then one could easily produce an array of sums (for a list, x, of numpy-compatible objects) like so:
> object_sums = [np.sum(anObject) for anObject in x]

Performance-wise, it should be no more or less efficient than having numpy somehow produce an array of sums from a single call to sum.  Readability-wise, it makes more sense because when you are treating objects separately, a *list* of them is more intuitive than a numpy.array, which is more-or-less treated as a single mathematical entity.