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Presentation . 2021
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Team-building and information flow for large groups such as online hackathons - Updated Feb 2021

Authors: Balli, Fabio;

Team-building and information flow for large groups such as online hackathons - Updated Feb 2021

Abstract

Between March and June 2020, 154’000 people took part in 136 hackathons against COVID. This equals 957’000 days of volunteering, for 17’700 projects released. 957’000 days of contributions equals 3’800 people working full time during a year. 3800 people developing 17’000 projects means each project must be realized by a person in < 3 month. Imagine these 154’000 people transform into one living organism. What could we dream this ‘super-body’ could achieve? The model presented replicates how the body works: as little information as needed (vs information overload): each person closely interacts with 14 people. coordinated work across teams (vs waste of resources): every individual contributes to one congruent whole. Leaders can rely on autonomous teams to support them. Individuals support each other to be part of a meaningful whole. There is a feeling of belonging, and projects sustain. Building on Laugeri's Three Contracts model, Leonardo 3.4.5 team profile, and matchmaking process

New slides added on February 2021.

Keywords

Emerging Change, group regulation, systemics, open access commons, team building, resource allocation, co-decision, game matchmaking, governance, social constructionism, open collaboration, Laugeri's Three Contracts method, Leonardo 3.4.5 team profile, stages of group development, imago adjustment

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Top 10%
Top 10%
Green