Milky’s Easy Peasy Bread recipeee

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Put the oven on at 180°C.

In a bowl, mix;

  • 3 cups self-raising flower
  • 1 teaspoon salt
  • 3 teaspoons caster sugar

Add 1 cup water, mix, get your hands sticky.

Plop mix on a baking tray or bread tin if you have one, shape/pat-down a tad, throw into the dark hole of heat.

Wait roughly 50 minuets, remove, stick on an airing tray or dish towel for roughly 10 mins (or sod that if you can’t wait), nom.

Add more sugar for a sweeter taste or don’t bother with either salt or sugar for a plain taste. I’m sure throwing in herbs would work also. I’ve added one cup of cocoa plus some chopped up baking chocolate and a bit extra sugar before. The dough can also be used as a pizza base – is easiest to use a rolling pin for that. You can substitute the water for milk which IMO gives for a nicer texture depending on how not skint you are.

Misc.

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The last I’m Sorry I Haven’t A Clue was a great, catch it before the next on Monday evening. “Diagnostic – A Welshman unsure if god exists. Realist – A catalogue of bottoms. … Newcastle – To drop an atom bomb on Peirs Morgan.”

Day made, mind blown: it’s Portal in ASCII

Enviro-Bear iPhone game. You’re a bear. With a car.

To have been a fly on the wall at the Imageshack offices the morning after.

average age of politicians

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Google; average age of politician and average age of elected officials, nowt really. politicians “average age”, some, but nothing meta.

Wolfram|Alpha; average age of elected officials and politicians age, nada. politicians, more but nothing that close.

DBpedia; lookup.dbpedia.org only works for single terms. I’m assuming there is some form of method to grep and display some part or levels of this query from the graph. The Wikipedia infoboxes DBpedia use (the info in the box) have DOBs and “In office” start/end dates roles.

Freebase is just ugh, wut these days.

Powerset; Sounds interesting;

“The Semantic Web promises to revolutionize access to information by adding machine-readable semantic information to content which is normally interpretable only by people. In addition, it will also revolutionize access to services by adding semantic information to create machine-readable service descriptions. This ambitious vision has been slow to take off because of a chicken and egg problem. Markup is required before people will build applications, applications are required before it is worth the hard work of doing markup. Natural language processing (NLP) has advanced to the point where it can break the impasse and open up the possibilities of the Semantic Web. First, NLP systems can now automatically create annotations from unstructured text. This provides the data that semantic web applications require. Second, NLP systems are themselves consumers of semantic web information and thus provide economic motivation for people to create and maintain such information. For example, a new generation of natural language search systems, as illustrated by Powerset, can take advantage of semantic web markup and ontologies to augment their interpretation of underlying textual content. They can also expose semantic web services directly in response to natural language queries.” link

(’42:35 – Ecosystem acceleration’ – “who in The Forest is the volunteer co-ordinator?”. Well, as Drupal [7] is going semantic…)

but a general search gives a big fail atm. Sentence matching gives handy results like “The average age of senators in 2007 was 62 years.
– Members of the 111th United States Congress” which you can dive into with the arrow button, but the Wikipedia article states [citation needed] for that info.

P.S.

“While 70 per cent of India?s population is below 40 years of age, 80 per cent of India?s politicians are over 70 years.” link

“The research is to develop, in conjunction with an existing community of researchers, a software environment for carrying out experimental research in corpus linguistics, with a particular emphasis on parsed text corpora. Corpora are collections of (transcribed) spoken and written passages, annotated in a number of different ways. A parsed corpus is one where the grammatical structure of each sentence is included. As corpora grow in size and complexity (typical parsed corpora are around the 1 million word mark), corpus linguistics research becomes increasingly dependent on computer software support. ” link

BBC Radio 4: Today programme: “Is the language politicians and policy makers use clear or confusing? The Public Administration Committee is today holding a public hearing on how the government uses, and misuses, language. Matthew Parris, Times columnist and former MP, and linguistics expert Professor David Crystal discuss the evidence they will give to the committee.” link, mp3

Urban Dictionary on the telly

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Woo, Urban Dictionary appeared on Keith Olbermann’s Countdown yesterday. With 593 up and 32 down in the clip, “Pullin’ a Palin” now stands at 3943 up, 978 down. Context on her jumping ship by quitting as Governor in an earlier segment here.

Old media and new [social] media, a false dichotomy, both being just terms of flow and feedback in one holistic system of systems, the vast and immane, interwoven, interacting, multivariate, multinational continuum of chaos, natch.

Illiberalism in politics

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“Two bills are currently in discussion in Holyrood that are of concern to CAAN members and supporters: The Criminal Justice and Licensing (Scotland) Bill and the Sexual Offences (Scotland) Bill. The former is included because of section 34 which bans extreme pornography; the latter is included because of a rejected recommendation to decriminalize sadomasochism.” link

“First there was the Dangerous Pictures Act, and then there was the Dangerous Cartoons Law. Now, courtesy of the Conservative Party, we could be in for new laws on ‘Dangerous Writings‘ …

One serious issue with this law, as with the law on extreme porn, is that unlike the Obscene Publications Act, it allows no exemptions for artistic merit or political intent.” link