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Non-Group: History, Members, Regions & Measurement
The Non-Group was a largely-secretive membership-limited formal grouping of Miami businesspeople that played a significant role in guiding local affairs in Greater Miami for decades. The group consisted of well-connected business elite, and was in the 1970s and 1980s widely regarded as the metro-region's main power broker, being described as a "shadow…
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Non-Group topic overview
The analysis highlights History, Members, Regions and Measurement as prominent areas in the source structure around Non-Group.
Related topics
99
Source areas
4
Connected nodes
103
Extracted relationships
228
Related term clusters
56
Bridge connections
103
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Members · 66 topics
Policies and projects promoted · 23 topics
History · 8 topics
Overview · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
You can skip this section if you’re here for content ideas and keyword inspiration.
Advanced semantic analysis
How Non-Group connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Non-Group shows recurring relationship patterns in the source. For example, Non-Group → Agro-Tech International, Allen Jr, AmeriFirst BankFrank Borman, Anthony Burns, Arvida Disney Corporation, Atico Savings BankWalter Alford, Babcock Jr, Barnett Bank, Barnett BankR, Barry UniversityCharles Olcott, Bassett, Beacon Council Miami Free, Blockbuster Entertainment Corporation, Bomar, Botifoli, Buddy, Budget, BurdinesJames McLamore, BurdinesStewart, Bureau Another extracted example is Non-Group → Armando Codina, Babcock Jr, Bob Graham, Borman, Bush, Capen Jr, Chapman, Charles, Colson, Companies, County, Crime, Dade County, Decade, December, Edwin Meese, Florida Legislature, Frank Borman, George, Governor Bob Graham. Use these groups to spot repeated connection types before inspecting the individual relationships.
Non-Group
Top relations
related to Members· 168
Non-Group → Agro-Tech International, Allen Jr, AmeriFirst BankFrank Borman, Anthony Burns, Arvida Disney Corporation, Atico Savings BankWalter Alford, Babcock Jr, Barnett Bank, Barnett BankR, Barry UniversityCharles Olcott, Bassett, Beacon Council Miami Free, Blockbuster Entertainment Corporation, Bomar, Botifoli, Buddy, Budget, BurdinesJames McLamore, BurdinesStewart, Bureau
related to Policies and projects promoted· 45
Non-Group → Armando Codina, Babcock Jr, Bob Graham, Borman, Bush, Capen Jr, Chapman, Charles, Colson, Companies, County, Crime, Dade County, Decade, December, Edwin Meese, Florida Legislature, Frank Borman, George, Governor Bob Graham
related to history· 8
Non-Group → Alvah Chapman Jr, Chapman, Co-founded, Dade County, Harry Hood Bassett, Maurice Ferré, Meetings, Miami
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 228 structured relationships around Non-Group. Examples in this analysis include Non-Group → Dissolved → 2000s and Non-Group → Formation → 1971. The table shows each extracted connection, where it came from and its confidence.
group through which leaders of the business community jointly steered local civic matters
1.00
infobox
Non-Group
Type
local NGO
1.00
infobox
Non-Group
related to history
Co-founded
0.60
section
Non-Group
related to history
Alvah Chapman Jr
0.60
section
Non-Group
related to history
Harry Hood Bassett
0.60
section
Non-Group
related to history
Chapman
0.60
section
Non-Group
related to history
Meetings
0.60
section
No triples extracted yet.
Related concept clusters Related term clusters
Related term clusters group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Non-Group bring nearby vocabulary together. In this analysis, examples include December, Joined and President. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
alvah chapman jr.
Former
Ceo
Joined
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
Increased
frank scruggs jr.
Former
Ceo
Joined
National
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
harper sibley jr.
Former
Ceo
Joined
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
Non-group
florida legislature
Executive
Chairman
Joined
December
Former
Attorney
Corporation
Miami
Non-group
Bank
President
Ceo
charles i. babcock jr.
Former
Ceo
Joined
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
Non-group
richard g. capen jr.
Former
Ceo
Joined
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
Non-group
bill allen jr.
Former
Ceo
Joined
December
President
Attorney
Chamber
Commerce
Miami
Chairman
Executive
Non-group
florida southern bell
Executive
Chairman
Joined
December
Former
Attorney
Corporation
Miami
Non-group
Bank
President
Ceo
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Non-Group, one of the stronger structural bridges in this analysis connects Non-Group with Members. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Non-Group
Nodes104
Edges103
Triples228
Avg. degree1.98
Density0.019231
Components1
Source & methodology
TTTA analyzes the structure around Non-Group to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Members, Regions & Measurement, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
