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Patterns of shared terms in lexicons based on concurrence C. Occurrences of terms in top 10 (a), 20 (b) 40 (c), and 60 (d) cohorts in all 12 cases in the stabilized case with β = 50. Terms occurring at least eight times are shown.
Source publication
Complex networks are often used to analyze written text and reports by rendering texts in the form of a semantic network, forming a lexicon of words or key terms. Many existing methods to construct lexicons are based on counting word co-occurrences, having the advantage of simplicity and ease of applicability. Here, we use a quantum semantics appro...
Contexts in source publication
Context 1
... on basis of co-occurrence, only about 50 out of the 90 terms appeared to be are of interest. These terms are also listed in Figures 7 and 8, where we can see that most of them, but not all, are obviously relevant for the topic of conceptual change. The frequency distribution of the 50 most frequently occurring terms in all 12 texts is shown in Figure 1 (panel a, left). ...
Context 2
... results in Figure 2 demonstrate the effect of optimization and differences between optimized concurrences and contingencies of term pairs. We will later (Figures 7 and 8, having first discussed the similarity of lexicons) return to the question of which terms are those ones yielding optimization, and at the same time, having significant roles in increasing the similarity of lexicons. ...
Context 3
... breakdown of key terms in top-cohorts as based on concurrence Q can be compared with similar breakdown when C-based correlation centrality is used as the basis of ranking. The breakdown in top cohorts in that case is shown in Figure 8. Comparing the results in Figure 7 to results in Figure 8, we see that with contingency C, key terms are not shared as frequently as for concurrence Q; many lexicons that overlap by sharing key terms with Q drop out for C. Comparison in Figures 7 and 8 finally answers the question of which terms are the significant, tunable terms that are responsible for the increased similarity of lexicons; they are the terms appearing in Figure 7 but not in Figure 8. ...
Context 4
... breakdown of key terms in top-cohorts as based on concurrence Q can be compared with similar breakdown when C-based correlation centrality is used as the basis of ranking. The breakdown in top cohorts in that case is shown in Figure 8. Comparing the results in Figure 7 to results in Figure 8, we see that with contingency C, key terms are not shared as frequently as for concurrence Q; many lexicons that overlap by sharing key terms with Q drop out for C. Comparison in Figures 7 and 8 finally answers the question of which terms are the significant, tunable terms that are responsible for the increased similarity of lexicons; they are the terms appearing in Figure 7 but not in Figure 8. In cases of cohorts of top 10 and 20, we can locate several such important, tunable terms. ...
Context 5
... breakdown of key terms in top-cohorts as based on concurrence Q can be compared with similar breakdown when C-based correlation centrality is used as the basis of ranking. The breakdown in top cohorts in that case is shown in Figure 8. Comparing the results in Figure 7 to results in Figure 8, we see that with contingency C, key terms are not shared as frequently as for concurrence Q; many lexicons that overlap by sharing key terms with Q drop out for C. Comparison in Figures 7 and 8 finally answers the question of which terms are the significant, tunable terms that are responsible for the increased similarity of lexicons; they are the terms appearing in Figure 7 but not in Figure 8. In cases of cohorts of top 10 and 20, we can locate several such important, tunable terms. ...
Context 6
... breakdown of key terms in top-cohorts as based on concurrence Q can be compared with similar breakdown when C-based correlation centrality is used as the basis of ranking. The breakdown in top cohorts in that case is shown in Figure 8. Comparing the results in Figure 7 to results in Figure 8, we see that with contingency C, key terms are not shared as frequently as for concurrence Q; many lexicons that overlap by sharing key terms with Q drop out for C. Comparison in Figures 7 and 8 finally answers the question of which terms are the significant, tunable terms that are responsible for the increased similarity of lexicons; they are the terms appearing in Figure 7 but not in Figure 8. In cases of cohorts of top 10 and 20, we can locate several such important, tunable terms. ...
Context 7
... cases of cohorts of top 10 and 20, we can locate several such important, tunable terms. On the other hand, the terms that appear in Figures 7 and 8 both, i.e., in practice nearly all terms in Figure 8, are not tunable, or their roles are not changed by tuning. Therefore, in seeking better overlap of lexicons, terms appearing in cohorts of top 10 and 20 terms in Figure 7 but not in Figure 8 are the most important ones. ...
Context 8
... cases of cohorts of top 10 and 20, we can locate several such important, tunable terms. On the other hand, the terms that appear in Figures 7 and 8 both, i.e., in practice nearly all terms in Figure 8, are not tunable, or their roles are not changed by tuning. Therefore, in seeking better overlap of lexicons, terms appearing in cohorts of top 10 and 20 terms in Figure 7 but not in Figure 8 are the most important ones. ...
Context 9
... the other hand, the terms that appear in Figures 7 and 8 both, i.e., in practice nearly all terms in Figure 8, are not tunable, or their roles are not changed by tuning. Therefore, in seeking better overlap of lexicons, terms appearing in cohorts of top 10 and 20 terms in Figure 7 but not in Figure 8 are the most important ones. ...
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Citations
... Such strong disciplinary fragmentation seems to be particularly apparent and typical in the human and behavioral sciences [23][24][25]. Another situation of interest, where the creation and generation of new knowledge is not necessarily of primary interest, but where differing disciplinary views about thematic topics can be recognized, is related to the disciplinary views of science education scholars [26,27] as well as science students, where student groups may have consensus views that differ from those of other student groups views, even when they have used the same study materials [28,29]. To address situations in which the knowledge or meaning structures of interest are complex systems of terms, concepts, or conceptions characteristic of the disciplinary group, it seems appropriate to use the expression "webs of beliefs" (compare e.g., ref. [30]), to be referred to briefly as WoBs in what follows. ...
Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views are not simple sets of opinions but rather complex webs of beliefs (WoBs). Here, we approach such consensus group formation through the agent-based model (ABM). Agents’ views are described as complex, extensive web-like structures resembling semantic networks, i.e., webs of beliefs. In the ABM introduced here, the agents’ interactions and participation in sharing their views are dependent on the similarity of the agents’ webs of beliefs; the greater the similarity, the more likely the interaction and sharing of elements of WoBs. In interactions, the WoBs are altered when agents seek consensus and consensus groups are formed. The consensus group formation depends on the agents’ sensitivity to the similarity of their WoBs. If their sensitivity is low, only one large and diffuse group is formed, while with high sensitivity, many separated and segregated consensus groups emerge. To conclude, we discuss how such results resemble the formation of disciplinary, scholarly consensus groups.
