Idealogs is a crowdsourcing project for creating literature reviews on controversial subjects. It integrates features of a wiki, blog, and digital object identification system into one simple website. Its main innovations are
- a model that explains the fundamental relationship between understanding and context;
- a wiki engine that implements that model; and
- a linking policy that makes it scalable.
The result is a platform for collaboratively summarizing discussions of complex topics.
- Context: There is a fundamental lack of context in electronic media. There is desperate need for information models rooted in context, not clickability.
- Public affairs journalism: Journalism is under siege, both figuratively and literally. Crowdsourcing is a unique mechanism for incentivizing original, fact-based reporting while discouraging derivative, emotion-driven clickbait.
- Misinformation: A recent analysis of how true and false news spreads on Twitter found that not only did falsehoods spread more widely and at a faster rate than the truth, but that it was humans, not robots, who were more likely to spread it. Frighteningly, a nontrivial percentage of peer-reviewed, quantitative research findings appear to be false as well. Literature reviews are a powerful framework for making sense of complex discourse, from its least to its most structured forms.
Idealogs is only possible due to some incredible open source projects.
- Framework: Django.
- Caching: memcached, pylibmc
- Database: PostgreSQL
- Task management: Celery, rabbitmq
- Server: nginx + gunicorn
- Markdown: Pandoc, with help from pandoc-sidenote
- Typography: Tufte CSS
- HTML post-processing: Beautiful Soup, Bleach
- Diffs, Patching: Diff Match Patch
- Search: Elasticsearch
- Metadata: Zotero