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---
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date: '2022-11-27T13:14:43'
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hypothesis-meta:
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created: '2022-11-27T13:14:43.604240+00:00'
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document:
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title:
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- Analysis_of_REF_impact.pdf
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flagged: false
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group: __world__
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hidden: false
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id: dTjBdG5VEe2sq0cgSuwbjw
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links:
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html: https://hypothes.is/a/dTjBdG5VEe2sq0cgSuwbjw
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incontext: https://hyp.is/dTjBdG5VEe2sq0cgSuwbjw/webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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json: https://hypothes.is/api/annotations/dTjBdG5VEe2sq0cgSuwbjw
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permissions:
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admin:
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- acct:ravenscroftj@hypothes.is
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delete:
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- acct:ravenscroftj@hypothes.is
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read:
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- group:__world__
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update:
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- acct:ravenscroftj@hypothes.is
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tags:
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- lda
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- comprehensive impact
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target:
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- selector:
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- end: 39662
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start: 39458
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type: TextPositionSelector
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- exact: Topic modelling was used to determine common topics across the wholecorpus.
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Sixty-five topics were found (of which 60 were used) using theApache Mallet
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Toolkit Latent Dirichlet Allocation (LDA) algorithm.
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prefix: s to answer specific challenges.
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suffix: 12Topics are based on the freque
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type: TextQuoteSelector
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source: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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text: The authors used LDA with k=60 across full text case studies. The Apache Mallet
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implementation was used.
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updated: '2022-11-27T13:14:43.604240+00:00'
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uri: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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user: acct:ravenscroftj@hypothes.is
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user_info:
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display_name: James Ravenscroft
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in-reply-to: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf
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tags:
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- lda
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- comprehensive impact
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- hypothesis
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type: annotation
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url: /annotations/2022/11/27/1669554883
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---
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<blockquote>Topic modelling was used to determine common topics across the wholecorpus. Sixty-five topics were found (of which 60 were used) using theApache Mallet Toolkit Latent Dirichlet Allocation (LDA) algorithm.</blockquote>The authors used LDA with k=60 across full text case studies. The Apache Mallet implementation was used.
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