that's not what i meant! - fran alexander

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  • 1. THATS NOT WHAT I MEANT!identifying, clarifying and brokering consensus over taxonomy terms and keywordsFran Alexander, Taxonomy Manager, Information and Archives, BBC@frangle*All views expressed here are entirely my own personal views and in no way represent the BBC or official BBC policy.

2. BBC Archive Centre2 million items of TV and video300,000 hours of audio6 million still photographs4 million pieces of sheet music500,000 documents4,000 loans per week 3. Overviewdivided by a common language the problem with wordsus and them categories and communitiessemantic politics brokering consensusclassification migration projectSharepoint projectontology projecttop tips 4. Apple, Orange, Blackberry, Next!ORANGE 4 5. Hes a real dog 6. What are the odds? trumpet violin French horn trombone 7. London China Brazil France 8. French Spanish Hebrew Italian 9. record object entity archive 10. archive backup record library 11. spook entity ghoul fairy 12. Top-down or bottom-up?traditional classifications were made bysubdivision of pre-set classesmodern taxonomies tend to work by clustering or groupingmore flexible systems, more closely related to reality, but need to understand users, viewpoints, contexts 13. Content engineersContent MetadataParametadata Meta-metadata(tag) Creator Taxonomy DateApprovedCanis lupus ResearcherTelclass (specialist 4/4/11JPtaxonomy)WolvesProductionBBC free tag 3/3/11-assistantGrey wolf CataloguerLonclass (archival 14/4/11 JPsleepingtaxonomy)/archives /archives team Special collections tag 12/1/11 JRWildlifeprogrammeCanidae Natural NHM taxonomy 11/6/11 CCHistoryMuseumWolf-spotting Member of Free tag/folksonomy12/4/11 _on holidaypublicwith Bob 14. Japan ese honth e J a p a n e s e c la s s ifie r H o n c la s s ifie s lo n g th ino b je c ts ; s tic k s c a n e s , p e n c ils , c a n d le s , tre e s , ro p e s ,h a ir, e tc . it c a n a ls o b e u s e d to c la s s ify d e a d s n a k e sa n d d rie d fis h , w ith a re lo n g a n d th in . B u t it a ls oin c lu d e s : m a rtia l a rts c o n te s ts w ith s ta ffs o r s w o rd s h its in b a s e b a ll s e r v e s in v o lle yb a ll a n d ra llie s in p in g p o n g ju d o m a tc h e sl ro le s o f ta p e te le p h o n e c a lls (w h ic h c o m e o v e r lo n g th in w ire s ) ra d io a n d T V p ro g ra m s (lik e p h o n e c a lls , b u t w ith o u t th e w ire s ) le tte rs (s c ro lls a re th in ) film s (b e c a u s e th e yre lik e ta p e ) in je c tio n s 15. D yirb a l c la s s ific a tio nB a yi: m e n , k a n g a ro o s, p o ssu m s , b a ts, m o stsn a ke s , m o st fish , so m e b ird s, m o s t in s e cts, th em o o n , sto rm s, ra in b o w s, b o o m e ra n g sB a la n : w o m e n , b a n d ic o o ts , d o g s , p la typ u s,e ch id n a , so m e sn a ke s, s o m e fish , m o s t b ird s,fireflie s, sco rp io n s, cricke ts, th e h a iry m a ry g ru b ,a n yth in g co n n e cte d w ith w a te r o r fire , su n a n dsta rsB a la m : a ll e d ib le fru it a n d th e p la n ts th a t b e a rth e m , fe rn s, h o n e y, cig a re tte s, w in e , c a keB a la : p a rts o f th e b o d y, m e a t, b e e s, w in d ,ya m sticks, so m e s p e a rs, m o st tre e s , g ra ss, m u d ,sto n e s, n o ise s , la n g u a g e . 16. B o rg e s C e le stia l e m p o riu m o f b e n e vo le n t kn o w le d g e o n th o se p a g e s it is w ritte n th a t a n im a ls a red ivid e d in to (a ) th o se th a t b e lo n g to th e e m p e ro r, (b )e m b a lm e d o n e s, (c) th o se th a t a re tra in e d , (d )su ck lin g p ig s, (e ) m e rm a id s, (f) fa b u lo u s o n e , (g ) stra yd o g s, (h ) th o se th a t a re in clu d e d in th is cla ssifica tio n ,(i) th o se th a t tre m b le a s if th e y w e re m a d , (j)in n u m e ra b le o n e s, (k ) th o se d ra w n w ith a ve ry fin eca m e ls h a ir b ru sh , (l) o th e rs, (m ) th o se th a t h a ve ju stb ro k e n a flo w e r va se , (n ) th o se th a t re se m b le flie sfrom a d ista n ce . 17. Sortedmeanings of words and labels how to make sure these are clearusers language communities, basiccategories, contexts how tounderstand their viewpointspractical methods to help you make decisions 18. 19 19. How enterprising 20. Everybody countsHow do you run a card sort with a million terms?How do you user test with 20,000 users? 21. Taking samplesselected 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 sessionsuser feedback and iterative changes 22. Whats your point?navigation?toolkit?mandatory or suggested?complete or selected? 23. Nice figurecommand-and-control?help and support?existing structuresexisting workflowsandprocesses 24. Top models 25. Class actthink about scope, purpose, userssimplification what can be ignoredshared understanding may be more than just getting labels right ontologies/programmes/ 2009-09-07.shtml 26. My domain is your kingdomwhat is the same as something else?what is Paris? an area, a city, a location in a film, an administrative districtdoes London include Stansted, Gatwick, and Luton?what happens if we get this wrong? 27. Noticeunderstand how fuzzy language can bethink precisely and clearlylearn to spot danger wordsbecome conversational negotiatorsdont underestimate how long you should spend checking definitions with usersdont 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 28. Find faultlabel errorspuns, jokes, word games to identify slippery wordsarguments and points of failure indicate lack of shared understandingsearch logs, analytics, questionnairesbe consistent in your own use of languagecategory errorsmiscellaneous change categories?frequent category errors by users - change categories?domain errorsnever be afraid to question shared understandingif you and your team dont know how it is supposed to work then no-one else will! 29. RefsAlexander, F. (2012) Building bridges: Linking diverse classification schemes aspart of a technology change project, Journal of Business Information Review, vol.29 no. 2, pp. 87-94., F. (2012) Assessing information taxonomies using epistemology andthe sociology of science, Journal of Documentation, Vol. 68, Issue 5. DOI: 10.1108/00220411211256058, F. (2009) Trying to please everyone: The taxonomist as politician., J. L. (1942) The Analytical Language of John Wilkins (El idioma analtico deJohn Wilkins).Bowker, G. and Star, S. L. (1999). Sorting Things Out: Classification and ItsConsequences.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 andOrganisational Effectiveness.Olson, H. (2002). The Power to Name.Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity. 30. In creditLabels: cards: Crowd (Osheaga 2009):