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

  

Seventy­Four Shades of Sustainability

Dmitry Zinoviev* and Zhen Zhu+

*Department of Mathematics and Computer Science+Department of Marketing

Suffolk University

Boston

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.

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.

Goals and Means

Method Workflow

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

Creator:cairo 1.8.10 (http://cairographi CreationDate:Mon Jan 20 17:28:58 2014 LanguageLevel:2

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]

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)

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)

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

Semantic MapsMaps 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 level; one cluster—one node; connection widths proportional to the overlap

A, B, and C to be addressed later

Semantic Network Stats

Network Nodes Density Modularity

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 24 0.043 6+3 0.58 0.53Mean 713 65 0.093 5+2 0.52 0.63

Average degree

centralityMajor/minor

clusters

Average clustering coefficient

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)

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 three-word 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)

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.

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

Three-Cluster Motif Networkscholars/consumers

scholars only

consumers only

Three-Cluster “Cluster” Network

Term clusters and their motifs co-belong to the same meta-clusters A, B, and C!

A, B, and C are semantic domains of sustainability

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)

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.

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%; scholars-dominated).

There are less common perceptions or interests share by both communities in the other two semantic domains.

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.

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