This document discusses a computational analysis of over 1.5 million words of descriptions of art objects from the British Museum written by Mary Dorothy George from 1935-1954. It includes the 100 most frequent words used in George's descriptions and notes that the data was published under a Creative Commons license.
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Satirical designs printed onto paper from engraved copper plates are a valuable source of behaviours, attitudes, controversies, and politics in late-Georgian London. Equally valuable to the historian are the detailed descriptions of some 12,000 of these satirical prints compiled by Mary Dorothy George and published as volumes five to eleven of the *Catalogue of Political and Personal Satires Preserved in the Department of Prints and Drawings in the British Museum* between 1935 and 1954. Now indexed as a database hosted on the British Museum website, George's interpretations underpin most historical research into these most beloved objects of British Art via keyword searches and SPAQL endpoint queries enabled by the transformation of her catalogue entries into digital data.
This paper describes research that uses George's descriptions as a proxy dataset for late-Georgian satirical prints, investigates patterns of behaviour in her descriptions, and explores how these corpus level patterns correlate with patterns of behaviour observable in hand-assembled collections of the satirical prints. Corpus level textual analysis (relative word frequencies, concordance measures, named entity recognition) and close object analysis of hand-assembled print collections are used side-by-side, with insights from each methodological approach used to generate insights that are then measured, tested, and enriched by the other.
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Library Carpentry: software skills training for library professionals, Chart...James Baker
Notes for a keynote I gave at the Chartered Institute for Library and Information Professionals Cataloguing and Indexing Group biennial conference, University of Swansea, 31 August - 2 September 2016.
Notes at https://gist.github.com/drjwbaker/96a32b70da2e03035272b6e5656696ad
Enabling Complex Analysis of Large-Scale Digital Collections: Humanities Rese...James Baker
Talk at Digital Humanities 2016 with Melissa Terras, James Hetherington, David Beavan, Anne Welsh, Helen O'Neill, Will Finley, Oliver Duke-Williams, Adam Farquhar, and Martin Zaltz Austwick.
Abstract http://dh2016.adho.org/abstracts/2584
Hard disks as archives of everyday lifeJames Baker
Deck for a talk I gave at Born digital big data and approaches for history and the humanities, School of Advanced Study (University of London), 8 June 2016.
Notes https://gist.github.com/drjwbaker/24ec7f744911800d51fb768cedb64510
Notes for a keynote I gave at the [Digital Humanities Early Career Forum](http://www.dhecf.group.shef.ac.uk/), University of Sheffield, 27 May 2016
My notes: http://jameswbaker.tumblr.com/post/144971807912/ditching-the-digital
The Hard Disk as the new Paper Archive: opportunities and challenges for hist...James Baker
Deck for a talk I gave at Digital History Seminar, University of Cambridge, 23 February 2016.
Notes at https://gist.github.com/drjwbaker/db1815e36ab64eb1a074
Deck for a talk I gave at Contemporary Political History in the Digital Age, Foreign & Commonwealth Office, 11 February 2016.
Notes at https://gist.github.com/drjwbaker/e01a3d03040c3ccdd4c1
This deck is for Library Carpentry week one, held 9 November 2015 at City University London. Lesson materials are at https://github.com/LibraryCarpentry/week-one-library-carpentry
Library Carpentry is generously funded by the [Software Sustainability Institute](http://software.ac.uk/). The Software Sustainability Institute cultivates world-class research with software. The Institute is based at the universities of Edinburgh, Manchester, Southampton and Oxford.
On Open Access monograph publishing for Arts, Humanities and Social Science R...James Baker
Deck for a talk I gave at the Open Access Week Open Access Seminar, University of Sussex, 20 October 2015
Talk at http://jameswbaker.tumblr.com/post/131273373912/on-open-access-monograph-publishing-for-arts
Deck for 3 minute talk I gave at Sussex Humanities Lab, Demo(s) or Die: Pecha Kucha, 28 September 2015
Words: http://jameswbaker.tumblr.com/post/130059926372/my-research-in-3-minutes
Acts of being in proxies for prints: People in the Catalogue of Political and...James Baker
Github repo with code, data, and viz: https://github.com/drjwbaker/2015-09_Mining-Utrecht
Satirical designs printed onto paper from engraved copper plates are a valuable source of behaviours, attitudes, controversies, and politics in late-Georgian London. Equally valuable to the historian are the detailed descriptions of some 12,000 of these satirical prints compiled by Mary Dorothy George and published as volumes five to eleven of the *Catalogue of Political and Personal Satires Preserved in the Department of Prints and Drawings in the British Museum* between 1935 and 1954. Now indexed as a database hosted on the British Museum website, George's interpretations underpin most historical research into these most beloved objects of British Art via keyword searches and SPAQL endpoint queries enabled by the transformation of her catalogue entries into digital data.
This paper describes research that uses George's descriptions as a proxy dataset for late-Georgian satirical prints, investigates patterns of behaviour in her descriptions, and explores how these corpus level patterns correlate with patterns of behaviour observable in hand-assembled collections of the satirical prints. Corpus level textual analysis (relative word frequencies, concordance measures, named entity recognition) and close object analysis of hand-assembled print collections are used side-by-side, with insights from each methodological approach used to generate insights that are then measured, tested, and enriched by the other.
Library Users of the Future... Or, projecting outward from that fringe of res...James Baker
Deck for a talk I gave at the Anybook Oxford Libraries Conference, Oxford University, 24 June 2015.
Notes at https://gist.github.com/drjwbaker/6c5011d595cabfa70e97
A workflow experiment; or (The Unexpected Virtue of Ignorance)James Baker
Deck for a talk I gave at IT's Personal: collecting, preserving and using personal digital archives, Digital Preservation Coalition, 28 April 2015.
Notes at https://gist.github.com/drjwbaker/91ab21a95a1dd73d6e96
Future Libraries: considering 'publishing', City University, London, 10 April...James Baker
Slides for a lecture I gave as part of the 'Libraries and Publishing in an Information Society' Masters module at City University, London, on 10 April 2015
Notes at https://gist.github.com/drjwbaker/9fbd71e4e4e232052265
Digital Research in the Arts and Humanities: some thoughts on what, why, and ...James Baker
Slides for a talk I gave at CHASE Digital Training Programme Opening Conference, Open University, 20 February 2015.
Notes: https://gist.github.com/drjwbaker/a95f4cee472af0d1773f
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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1.5 million words of Mary Dorothy George: a computational approach to curatorial voice and legacy descriptions of art objects
1. 1.5 million words of
Mary Dorothy George
A computational approach to curatorial voice
and legacy descriptions of art objects
James Baker Andrew Salway
Senior Lecturer in Digital History and Archives Research Fellow in DH
University of Sussex + Sussex Humanities Lab
This work is licensed under a Creative Commons Attribution 4.0 International License.
Exceptions: quotations, embeds from external sources, logos, marked images, slides marked
with an alternative licence.
curatorialvoice.github.io
7. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says (4816),
who (4815), On (4699), he (4683), man (4500), two (4466), him (4310), hand
(4272), head (4223), holding (3885), one (3841), holds (3773), as (3545), In
(3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656), arm
(1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625), BMSat
(1592), round (1589), men (1565), dressed (1559), them (1558), background
(1542), extreme (1526), long (1514), His (1495), its (1433), looks (1431),
ground (1398), stand (1362), Lord (1352), under (1340), Two (1337), See
(1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262), seated
(1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
8. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says (4816),
who (4815), On (4699), he (4683), man (4500), two (4466), him (4310), hand
(4272), head (4223), holding (3885), one (3841), holds (3773), as (3545), In
(3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656),
arm (1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625),
BMSat (1592), round (1589), men (1565), dressed (1559), them (1558),
background (1542), extreme (1526), long (1514), His (1495), its (1433), looks
(1431), ground (1398), stand (1362), Lord (1352), under (1340), Two (1337),
See (1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262),
seated (1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
9. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says (4816),
who (4815), On (4699), he (4683), man (4500), two (4466), him (4310), hand
(4272), head (4223), holding (3885), one (3841), holds (3773), as (3545), In
(3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656), arm
(1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625), BMSat
(1592), round (1589), men (1565), dressed (1559), them (1558), background
(1542), extreme (1526), long (1514), His (1495), its (1433), looks (1431),
ground (1398), stand (1362), Lord (1352), under (1340), Two (1337), See
(1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262), seated
(1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
10. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says
(4816), who (4815), On (4699), he (4683), man (4500), two (4466), him (4310),
hand (4272), head (4223), holding (3885), one (3841), holds (3773), as (3545),
In (3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656), arm
(1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625), BMSat
(1592), round (1589), men (1565), dressed (1559), them (1558), background
(1542), extreme (1526), long (1514), His (1495), its (1433), looks (1431),
ground (1398), stand (1362), Lord (1352), under (1340), Two (1337), See
(1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262), seated
(1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
11. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says (4816),
who (4815), On (4699), he (4683), man (4500), two (4466), him (4310), hand
(4272), head (4223), holding (3885), one (3841), holds (3773), as (3545), In
(3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656), arm
(1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625), BMSat
(1592), round (1589), men (1565), dressed (1559), them (1558), background
(1542), extreme (1526), long (1514), His (1495), its (1433), looks (1431),
ground (1398), stand (1362), Lord (1352), under (1340), Two (1337), See
(1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262), seated
(1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
12. @j_w_baker
the (67,434), a (64,436), of (32,450), and (32,020), is (24,050), in (22,078), his
(20,520), with (18,164), on (17,568), to (15,775), The (11,659), are (11,161), A
(8881), by (7721), inscribed (7458), from (7268), which (6971), left (6596), right
(6516), an (6428), at (6366), He (5571), stands (5377), her (4973), says (4816),
who (4815), On (4699), he (4683), man (4500), two (4466), him (4310), hand
(4272), head (4223), holding (3885), one (3841), holds (3773), as (3545), In
(3317), behind (3083), large (3079), No (3000), other (2960), profile (2949),
wearing (2880), hat (2862), saying (2832), up (2820), wears (2809), Behind
(2792), has (2641), back (2573), sits (2328), it (2270), for (2177), out (2156),
over (2154), table (2099), woman (1937), three (1892), towards (1873), or
(1844), their (1815), design (1754), small (1711), that (1661), paper (1656), arm
(1655), but (1635), Fox (1631), hands (1631), each (1629), title (1625), BMSat
(1592), round (1589), men (1565), dressed (1559), them (1558), background
(1542), extreme (1526), long (1514), His (1495), its (1433), looks (1431),
ground (1398), stand (1362), Lord (1352), under (1340), Two (1337), See
(1315), wall (1311), John (1306), &c (1296), have (1276), arms (1262), seated
(1258), above (1255), She (1244), beside (1244), I (1234), Below (1221)
Figure 1: The 100 most frequent words in the George corpus, with frequencies
in brackets.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
13. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would, have,
we, said, can, will, there, been, when, they, this, could,
what, time, It, do, so, But, know, then, more, any, as, no, all,
because, people, er, should, now, years, it's, got, work,
about, if, such, get, did, or, don't, think, she, way, but, may,
your, than, new, me, even, If, well, year, go, We, And, You,
some, only, our, how, need, per, might, made, going, used,
I'm, use, good, want, just, really, thought, It's, local, What,
must, government, something, went, course, after, too,
system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
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curatorialvoice.github.io
14. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would, have,
we, said, can, will, there, been, when, they, this, could,
what, time, It, do, so, But, know, then, more, any, as, no, all,
because, people, er, should, now, years, it's, got, work,
about, if, such, get, did, or, don't, think, she, way, but, may,
your, than, new, me, even, If, well, year, go, We, And, You,
some, only, our, how, need, per, might, made, going, used,
I'm, use, good, want, just, really, thought, It's, local, What,
must, government, something, went, course, after, too,
system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
15. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would, have,
we, said, can, will, there, been, when, they, this, could,
what, time, It, do, so, But, know, then, more, any, as, no, all,
because, people, er, should, now, years, it's, got, work,
about, if, such, get, did, or, don't, think, she, way, but, may,
your, than, new, me, even, If, well, year, go, We, And, You,
some, only, our, how, need, per, might, made, going, used,
I'm, use, good, want, just, really, thought, It's, local, What,
must, government, something, went, course, after, too,
system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
16. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would, have,
we, said, can, will, there, been, when, they, this, could,
what, time, It, do, so, But, know, then, more, any, as, no, all,
because, people, er, should, now, years, it's, got, work,
about, if, such, get, did, or, don't, think, she, way, but, may,
your, than, new, me, even, If, well, year, go, We, And, You,
some, only, our, how, need, per, might, made, going, used,
I'm, use, good, want, just, really, thought, It's, local, What,
must, government, something, went, course, after, too,
system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
17. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would, have,
we, said, can, will, there, been, when, they, this, could,
what, time, It, do, so, But, know, then, more, any, as, no, all,
because, people, er, should, now, years, it's, got, work,
about, if, such, get, did, or, don't, think, she, way, but, may,
your, than, new, me, even, If, well, year, go, We, And, You,
some, only, our, how, need, per, might, made, going, used,
I'm, use, good, want, just, really, thought, It's, local, What,
must, government, something, went, course, after, too,
system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
18. @j_w_baker
was, that, I, be, for, to, you, had, it, were, not, would,
have, we, said, can, will, there, been, when, they, this,
could, what, time, It, do, so, But, know, then, more, any, as,
no, all, because, people, er, should, now, years, it's, got,
work, about, if, such, get, did, or, don't, think, she, way, but,
may, your, than, new, me, even, If, well, year, go, We, And,
You, some, only, our, how, need, per, might, made, going,
used, I'm, use, good, want, just, really, thought, It's, local,
What, must, government, something, went, course, after,
too, system, like, came, So
Figure 2: a set of negative keywords extracted from the George corpus, i.e.
words occurring unusually infrequently compared with the British National
Corpus.
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
19. @j_w_baker
Figure 3: Frequencies of some common verbs in the George corpus and their
troponyms (combining all forms of each verb).
The data published by ResearchSpace under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
curatorialvoice.github.io
30. 1.5 million words of
Mary Dorothy George
A computational approach to curatorial voice
and legacy descriptions of art objects
James Baker Andrew Salway
Senior Lecturer in Digital History and Archives Research Fellow in DH
University of Sussex + Sussex Humanities Lab
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curatorialvoice.github.io