that's not what i meant! - fran alexander

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THAT'S NOT WHAT I MEANT! identifying, clarifying and brokering consensus over taxonomy terms and keywords Fran Alexander, Taxonomy Manager, Information and Archives, BBC @frangle http://www.vocabcontrol.com *All views expressed here are entirely my own personal views and in no way represent the BBC or official BBC policy.

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Page 1: That's not what I meant! - Fran Alexander

THAT'S NOT WHAT I MEANT!identifying, clarifying and brokering consensus over taxonomy terms and keywords

Fran Alexander, Taxonomy Manager, Information and Archives, BBC@franglehttp://www.vocabcontrol.com

*All views expressed here are entirely my own personal views and in no way represent the BBC or official BBC policy.

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BBC Archive Centre

2 million items of TV and video300,000 hours of audio6 million still photographs4 million pieces of sheet music500,000 documents

4,000 loans per week

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divided by a common language – the problem with wordsus and them – categories and communitiessemantic politics – brokering consensus

classification migration projectSharepoint projectontology project

top tips

Overview

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4

ORANGE

Apple, Orange, Blackberry, Next!

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He's a real dog

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What are the odds?

• trumpet

• violin

• French horn

• trombone

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• London

• China

• Brazil

• France

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• French

• Spanish

• Hebrew

• Italian

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• record

• object

• entity

• archive

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• archive

• backup

• record

• library

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• spook

• entity

• ghoul

• fairy

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Top-down or bottom-up?

traditional classifications were made by subdivision of pre-set classes

modern taxonomies tend to work by clustering or grouping

more flexible systems, more closely related to reality, but need to understand users, viewpoints, contexts

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Content engineers

Content Metadata

(tag)

Parametadata – “Meta-metadata”

Creator Taxonomy Date Approved

Wildlife programme

Canis lupus Researcher Telclass (specialist taxonomy)

4/4/11 JP

Wolves Production assistant

BBC free tag 3/3/11 -

Grey wolf sleeping

Cataloguer Lonclass (archival taxonomy)

14/4/11 JP

/archives /archives team Special collections tag 12/1/11 JR

Canidae Natural History Museum

NHM taxonomy 11/6/11 CC

Wolf-spotting on holiday with Bob

Member of public

Free tag/folksonomy 12/4/11 _

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Japanese hon

“the Japanese classifier ‘Hon’ classifies long thinobjects; sticks canes, pencils, candles, trees, ropes,hair, etc. it can also be used to classify dead snakesand dried fish, with are long and thin. But it alsoincludes:

martial arts contests with staffs or swords hits in baseball serves in volleyball and rallies in pingpong judo matches roles of tape telephone calls (which come over long thin

wires) radio and TV programs (like phone calls, but

without the wires) letters (scrolls are thin) films (because they’re like tape) injections

l

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Dyirbal classification

Bayi: men, kangaroos, possums, bats, mostsnakes, most fish, some birds, most insects, themoon, storms, rainbows, boomerangs

Balan: women, bandicoots, dogs, platypus,echidna, some snakes, some fish, most birds,fireflies, scorpions, crickets, the hairy mary grub,anything connected with water or fire, sun andstars

Balam: all edible fruit and the plants that bearthem, ferns, honey, cigarettes, wine, cake

Bala: parts of the body, meat, bees, wind,yamsticks, some spears, most trees, grass, mud,stones, noises, language.

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Borges Celestial emporium of benevolent knowledge

“… on those pages it is written that animals aredivided into (a) those that belong to the emperor, (b)embalmed ones, (c) those that are trained, (d)suckling pigs, (e) mermaids, (f) fabulous one, (g) straydogs, (h) those that are included in this classification,(i) those that tremble as if they were mad, (j)innumerable ones, (k) those drawn with a very finecamel’s hair brush, (l) others, (m) those that have justbroken a flower vase, (n) those that resemble fliesfrom a distance.”

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Sorted

meanings of words and labels – how to make sure these are clear

users' language communities, basic categories, contexts – how to understand their viewpoints

practical methods to help you make decisions

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19

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How enterprising

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Everybody counts

How do you run a card sort with a million terms?

How do you user test with 20,000 users?

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Taking samples

selected representatives from different communitiesran a workshop on high level categoriestop-down and bottom-up - mixed approachsections assigned to editor/s and SMEsall-editors regular discussion sessions user feedback and iterative changes

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What's your point?

navigation?toolkit?mandatory or suggested?complete or selected?

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Nice figure

command-and-control?help and support?

existing structures existing workflows

and processes

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Top models

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Class act

think about scope, purpose, users

simplification – what can be ignored

shared understanding may be more than just getting labels right

http://www.bbc.co.uk/ontologies/programmes/2009-09-07.shtml

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My domain is your kingdom

what is the same as something else?

what is Paris? an area, a city, a location in a film, an administrative district

does London include Stansted, Gatwick, and Luton?

what happens if we get this wrong?

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Notice

understand how fuzzy language can bethink precisely and clearly learn to spot “danger” wordsbecome “conversational negotiators”don’t underestimate how long you should

spend checking definitions with usersdon’t underestimate importance of iteration

– even ripping up and starting againmore people you involve, better able to get a

clear view of an areaknow when to stop and just decide

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Find fault

label errorspuns, jokes, word games – to identify slippery words

arguments and points of failure – indicate lack of shared understanding

search logs, analytics, questionnaires

be consistent in your own use of language

category errorsmiscellaneous – change categories?

frequent category errors by users - change categories?

domain errorsnever be afraid to question shared understanding

if you and your team don’t know how it is supposed to work then no-one else will!

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RefsAlexander, F. (2012) Building bridges: Linking diverse classification schemes as part of a technology change project, Journal of Business Information Review, vol. 29 no. 2, pp. 87-94. http://bir.sagepub.com/content/29/2/87.abstract

Alexander, F. (2012) Assessing information taxonomies using epistemology andthe sociology of science, Journal of Documentation, Vol. 68, Issue 5. DOI: 10.1108/00220411211256058 http://www.emeraldinsight.com/journals.htm?issn=0022-0418&volume=68&issue=5&articleid=17036853&show=pdfAlexander, F. (2009) Trying to please everyone: The taxonomist as politician. http://bit.ly/gC7MiWBorges, J. L. (1942) The Analytical Language of John Wilkins (El idioma analítico de John Wilkins). Bowker, G. and Star, S. L. (1999). Sorting Things Out: Classification and Its Consequences. Brown, J. S. and Duguid, P. (2000). The Social Life of Information. Lakoff, G. (1987). Women, Fire and Dangerous Things. Lambe, P. (2007). Organising Knowledge: Taxonomies. Knowledge and Organisational Effectiveness. Olson, H. (2002). The Power to Name.Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity.

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In credit

Labels:

http://www.flickr.com/photos/mrsmagic/5870198525/

Moo cards: http://www.flickr.com/photos/philgyford/247592709/sizes/l/in/photostream/

Concert Crowd (Osheaga 2009): http://www.flickr.com/photos/anirudhkoul/3786725982/sizes/l/in/photostream/