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Crowdsourcing (též wisdom of the crowd, „moudrost davů“) je novotvar pro označení způsobu dělby práce, při které se úkol obvykle vykonávaný zaměstnanci nebo kontraktory v rámci outsourcingu zadá blíže nespecifikované skupině lidí jako všeobecná výzva. Například se veřejnost vyzve na spolupráci při vývoji nové technologie, uskutečnění designérské úlohy…
The analysis highlights Původ, Kontroverze and Příklad projektu as prominent areas in the source structure around Crowdsourcing.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Crowdsourcing shows recurring relationship patterns in the source. For example, Crowdsourcing → Dává, Geo-Wiki, Je, Například, Picture Pile, Projekt Picture Pile, Registrovaný Another extracted example is Crowdsourcing → Douglas Rushkoff, Etické, Například, Některé, Wired News, Zakladatel Wikipedie Jimmy Wales. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
crowdsourcingu například projektu projekt termín práce jsou skupině lidí technologie více správnou náklady může důsledky zadá jako úlohy velkého díky
TTTA extracted 22 structured relationships around Crowdsourcing. Examples in this analysis include Crowdsourcing → related to Externí odkazy → Obrázky and Crowdsourcing → related to Externí odkazy → Wikimedia Commons. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Crowdsourcing | related to Externí odkazy | Obrázky | 0.60 | section |
| Crowdsourcing | related to Externí odkazy | Wikimedia Commons | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Etické | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Například | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Douglas Rushkoff | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Wired News | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Zakladatel Wikipedie Jimmy Wales | 0.60 | section |
| Crowdsourcing | related to Kontroverze | Některé | 0.60 | section |
| Crowdsourcing | related to Příklad projektu | Picture Pile | 0.60 | section |
| Crowdsourcing | related to Příklad projektu | Registrovaný | 0.60 | section |
| Crowdsourcing | related to Příklad projektu | Například | 0.60 | section |
| Crowdsourcing | related to Příklad projektu | Projekt Picture Pile | 0.60 | section |
The concept neighborhoods around Crowdsourcing bring nearby vocabulary together. In this analysis, examples include Článku, Crowdsourcingu and Kontraktory. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Crowdsourcing, one of the stronger structural bridges in this analysis connects Crowdsourcing with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Crowdsourcing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Původ, Kontroverze & Příklad projektu, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Crowdsourcing · CS edition · Analysis: TopicsToTalkAbout