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Conceptual Structure of 
Sustainability: Social and Scholarly 
Perspectives
Dmitry Zinoviev* and Zhen Zhu+
Department of M...
Seventy­Four Shades of 
Sustainability
Dmitry Zinoviev* and Zhen Zhu+
Department of Mathematics and Computer Science

*

D...
What Is Sustainability?






“...using resources to meet the needs of the present
without compromising the ability of ...
Goals and Means

Goals:
Map the semantic mindspace regarding the concept of
sustainability.
 Develop a transferrable mapp...
Method Workflow
Creator:cairo 1.8.10 (http://cairographi
CreationDate:Mon Jan 20 17:28:58 2014
LanguageLevel:2



Acquire...
Data Framework and Sources







Paper keywords from EBSCO academic database (supplied by
authors)—scholarly aspect [K...
Term Structure






Select 600–700* most
frequently used terms from
each data source
Only 6% of the terms are
used in ...
Term-Artifact Structure


Seven incidence matrices:




Profiles of sustainability-related LJ communities vs
interests ...
Similarity Calculation


Generalized similarity [-1...1] between terms/artifacts
(Kovacz 2010):








Two terms are...
Semantic Maps
Maps TA and TK are very similar. Only TP is shown to save space.
Clustering








The maps have a clear
clustering structure
Extract clusters of
terms from each map
One map—one leve...
Semantic Network Stats
Network
Keywords, by paper
(KP)
Keywords, by author
(KA)
Subject Tags, by
paper (TP)
Subject Tags, ...
Motif Extraction


Motifs—“meta-terms” describing a semantic cluster



Identified via Amazon Mechanical Turk (mTurk) by...
Motif Examples
LJ Core, cluster “262”: SOC-/12, POLIT-/10, LIB-/10,
DEMOCR-/9, HUM-/8, RIGHT-/7, HIPPY-/5, GOVERN-/4,
FREE...
Bipartite Network Again!



Motifs and semantic term clusters form a bipartite network
Generalized similarities between ...
Three-Cluster Motif Network
scholars/consumers
scholars only

consumers only
Three-Cluster “Cluster” Network





Term clusters and their motifs co-belong to the same metaclusters A, B, and C!
A, B...
Sustainability Lattice








A, B, and C are
semantic domains,
each formed by term
clusters and respective
motifs
A:...
Marketing and Multidisciplinarity




Lack of congruence between the keywords (KA/KP) and
subject tags (TA/TP) layers ma...
Scholars vs Consumers










Drastically different patterns of shared motifs by scholars
and consumers.
The two co...
Knowledge Aggregation






The average degree centrality, network density, and
clustering coefficient increase in the ...
Conclusion
(1) We developed a transferable semi-automated framework
for multifaceted analysis of “fuzzy” concepts, such as...
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What is "sustainability" from the scholars' and consumers' points of view?

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Sustainability

  1. 1. Conceptual Structure of  Sustainability: Social and Scholarly  Perspectives Dmitry Zinoviev* and Zhen Zhu+ Department of Mathematics and Computer Science * Department of Marketing + Suffolk University Boston    
  2. 2. Seventy­Four Shades of  Sustainability Dmitry Zinoviev* and Zhen Zhu+ Department of Mathematics and Computer Science * Department of Marketing + Suffolk University Boston    
  3. 3. What Is Sustainability?    “...using resources to meet the needs of the present without compromising the ability of future generations to meet their own needs...” Refers to agriculture, material engineering, energy, economics, political science, sociology, management. Silos of knowledge emerged across distinct disciplines and divergence in perceptions of sustainability becomes noticeable.
  4. 4. Goals and Means Goals: Map the semantic mindspace regarding the concept of sustainability.  Develop a transferrable mapping tool.  Means: Collect and analyze term data available from various sustainability-related sources, using semantic network analysis. 
  5. 5. Method Workflow Creator:cairo 1.8.10 (http://cairographi CreationDate:Mon Jan 20 17:28:58 2014 LanguageLevel:2  Acquire term data from a variety of data source  Select most commonly used terms  Evaluate term similarity  Cluster terms, based on similarity  Extract motifs (meta-terms) using crowdsourcing via Amazon Mechanical Turk
  6. 6. Data Framework and Sources    Paper keywords from EBSCO academic database (supplied by authors)—scholarly aspect [KWD] Paper subject tags from EBSCO academic database (supplied by editors)—scholarly aspect [TAG] Interests from LiveJournal (supplied by sustainability-related communities' moderators and individual bloggers, both involved in the sustainability-related communities and not)—consumer aspect [LJ]
  7. 7. Term Structure    Select 600–700* most frequently used terms from each data source Only 6% of the terms are used in all three term corpora The overlap between two scholarly corpora is only 25%! (Marketing to blame?) * (limited by the performance of the similarity calculation procedure)
  8. 8. Term-Artifact Structure  Seven incidence matrices:   Profiles of sustainability-related LJ communities vs interests (CORE) Profiles of LJ bloggers in sustainability-related communities vs interests (PPL)  Profiles of random LJ bloggers vs interests (BASE)  EBSCO papers vs keywords (KP)  EBSCO authors vs keywords (via papers; KA)  EBSCO papers vs subject tags (TP)  EBSCO authors vs subject tags (via papers; TA)
  9. 9. Similarity Calculation  Generalized similarity [-1...1] between terms/artifacts (Kovacz 2010):     Two terms are similar if they are associated with similar artifacts. Two artifacts are similar if they are associated with similar terms. Iterative procedure calculates two similarity matrices: one for artifacts (not used) and another for terms Evaluated for each incidence matrix
  10. 10. Semantic Maps Maps TA and TK are very similar. Only TP is shown to save space.
  11. 11. Clustering     The maps have a clear clustering structure Extract clusters of terms from each map One map—one level; one cluster—one node; connection widths proportional to the overlap A, B, and C to be addressed later
  12. 12. Semantic Network Stats Network Keywords, by paper (KP) Keywords, by author (KA) Subject Tags, by paper (TP) Subject Tags, by author (TA) Communities’ interests (CORE) Members’ interests (PPL) Random bloggers’ interests (BASE) Mean Nodes Average degree centrality Density Major/minor clusters Modularity Average clustering coefficient 753 162 0.228 4+3 0.1 0.76 679 16 0.027 8+7 0.67 0.53 755 42 0.06 6 0.59 0.62 666 48 0.067 5+1 0.59 0.63 769 107 0.148 4 0.56 0.74 752 57 0.079 5+2 0.58 0.62 615 713 24 65 0.043 0.093 6+3 5+2 0.58 0.52 0.53 0.63
  13. 13. Motif Extraction  Motifs—“meta-terms” describing a semantic cluster  Identified via Amazon Mechanical Turk (mTurk) by asking:    “Describe the following group of 25 / 50 words with a single most suitable word or a two-word or threeword phrase.” 100 mTurk workers per cluster (50 for top 25 terms and 50 for top 50 terms) Normalize responses (remove typos, Anglicisms, stopwords, punctuation; do stemming; select stems that are on both 25- and 50-word lists)
  14. 14. Motif Examples LJ Core, cluster “262”: SOC-/12, POLIT-/10, LIB-/10, DEMOCR-/9, HUM-/8, RIGHT-/7, HIPPY-/5, GOVERN-/4, FREEDOM-/4 LJ Core, cluster “260”: GREEN-/16, ENVIRON-/15, LIV-/14, NAT-/11, ECO-FRIENDLY-/8, FRIEND-/6 LJ Core, cluster “163”: ENVIRON-/24, ENERGY-/16, GREEN-/12, NAT-/9, SCI-/7, EAR-/7, RENEW-/6 LJ Core, cluster “84”: HEAL-/11, FOOD-/10, LIV-/9, HEALTHY-/8, VEGET-/7, ORG-/5 Numbers show the total number of times the stem was used by the mTurk workers with respect to the cluster.
  15. 15. Bipartite Network Again!   Motifs and semantic term clusters form a bipartite network Generalized similarities between motifs and term clusters can be calculated:   Clustered network of motifs, based on their generalized similarity Clustered network of semantic term clusters, based on their generalized similarity
  16. 16. Three-Cluster Motif Network scholars/consumers scholars only consumers only
  17. 17. Three-Cluster “Cluster” Network   Term clusters and their motifs co-belong to the same metaclusters A, B, and C! A, B, and C are semantic domains of sustainability
  18. 18. Sustainability Lattice     A, B, and C are semantic domains, each formed by term clusters and respective motifs A: “Environmental / Farming” B: “Politics / Economics” C: “Healthy Lifestyle” (absent from the EBSCO keywords levels)
  19. 19. Marketing and Multidisciplinarity   Lack of congruence between the keywords (KA/KP) and subject tags (TA/TP) layers may indicate a marketing element: authors may chose keywords to target potential readers, while tag editors concentrate more on the substance of the papers Lack of congruence between the keywords-by-author (KA) and keywords-by-paper (KP) layers is probably the result of multidisciplinary cooperation, where authors from different disciplines (not unlike us ☺) infuse keywords from their “native” disciplines.
  20. 20. Scholars vs Consumers      Drastically different patterns of shared motifs by scholars and consumers. The two communities shared the largest common grounds in the Environment / Farming domain (more than 40% of the motifs). Not so good for Healthy Lifestyle domain (about 35.5%; consumers-dominated). Bad for Politics / Economics domain (28%; scholarsdominated). There are less common perceptions or interests share by both communities in the other two semantic domains.
  21. 21. Knowledge Aggregation    The average degree centrality, network density, and clustering coefficient increase in the directions KA→TP→TA and BASE→PPL→CORE The aggregating networks: TA/TP and CORE—are denser (have more similarity connections between individual terms) and less structured (have more transitive similarity connections) than “grassroot” networks, KA/KP and BASE/PPL. Similarities emerge that are not seen to individual consumers and researchers, but are captured by community moderators and subject tag editors over time.
  22. 22. Conclusion (1) We developed a transferable semi-automated framework for multifaceted analysis of “fuzzy” concepts, such as “sustainability,” “resilience,” “complexity,” “success” (2) We applied the framework to the concept of “sustainability” (3) We identified 74 motifs, describing sustainability and grouped into three major semantic domains (4) We discovered differences between scholarly and consumer-oriented views of sustainability

What is "sustainability" from the scholars' and consumers' points of view?

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