English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015,only 2,513 were enacted. We developed a machine learning approach toforecasting the probability that any bill will become law. Starting in 2001with the 107th Congress, we trained models on data from previous Congresses,predicted all bills in the current Congress, and repeated until the 113thCongress served as the test. For prediction we scored each sentence of a billwith a language model that embeds legislative vocabulary into ahigh-dimensional, semantic-laden vector space. This language representationenables our investigation into which words increase the probability ofenactment for any topic. To test the relative importance of text and context,we compared the text model to a context-only model that uses variables such aswhether the bill's sponsor is in the majority party. To test the effect ofchanges to bills after their introduction on our ability to predict their finaloutcome, we compared using the bill text and meta-data available at the time ofintroduction with using the most recent data. At the time of introductioncontext-only predictions outperform text-only, and with the newest datatext-only outperforms context-only. Combining text and context always performsbest. We conducted a global sensitivity analysis on the combined model todetermine important variables predicting enactment.

Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model