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Re: 48-Hour Consensus Call: InstateLongdesc CP Update

From: Silvia Pfeiffer <silviapfeiffer1@gmail.com>
Date: Wed, 19 Sep 2012 21:16:59 +1000
Message-ID: <CAHp8n2kVNvRsxhNdqUgoZA4gNCE-TXUoHPUniZvVr59j+khO2A@mail.gmail.com>
To: Laura Carlson <laura.lee.carlson@gmail.com>
Cc: Geoff Freed <geoff_freed@wgbh.org>, John Foliot <john@foliot.ca>, Sam Ruby <rubys@intertwingly.net>, HTML Accessibility Task Force <public-html-a11y@w3.org>
On Wed, Sep 19, 2012 at 9:05 PM, Laura Carlson
<laura.lee.carlson@gmail.com> wrote:
> Hi Geoff,
> On Tue, Sep 18, 2012 at 4:52 PM, Geoff Freed wrote:
>> To bolster John's point, I'd like to say that there are efforts-- intense efforts-- underway to improve the quality of image descriptions.  For example, NCAM expends a large amount of time and effort every year training publishers, teachers and others in the art of writing long image descriptions.  I'm sure there are several others on this list who can (and, I hope, will) raise their hands and say the same thing about what they do.
> I teach how to write alternate text and longdesc in one-on-one
> settings as part of my daily job duties and formally in workshops
> multiple times a year. Debi Orton attested in the last HTML WG survey
> on ISSUE-30 that she always teaches longdesc and how to use it
> effectively.
> We have longdesc support base existing in the form of authoring tools,
> documentation, tutorials, books, etc. all of which is all a part of
> our evidence so I will not repeat it again here.

This is all great and really necessary. But can we quantify the
effect? It would, for example, be a good argument if we were able to
say that 4 years ago we made this analysis and only 0.1% of images hat
a @longdesc and 99% of those were wrong, while now 1% of images have
one and 70% of them are correctly implemented. Just making up numbers
here - but something like this would be really helpful.

Received on Wednesday, 19 September 2012 11:17:50 UTC

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