IOTO

Overview

This project aims to create new tools to extract insights with predictive or decision making value from the output of legislative bodies. It will investigate whether techniques from signal analysis or related mathematical fields can be used to help construct these tools. The goal is to leverage public data from legislative bodies to reveal what in the legislative activity is deserving if attention from a policy point of view, either by connecting with a known policy ontology (such as the comparative agendas codebook), or by surfacing issues that should be connected to a known ontology.

Background

Goverlytics^® seeks to produce low-dimensional representations of legislative activity to: 1) make politics accessible to a broader public; and to 2) increase focus on policy goals. The model for Goverlytics^® is sports analytics, which has transformed the way in which sports are understood and consumed. Goverlytics^® analyzes data generated during legislative sessions: attendance, documents, transcripts, vote tallies, audio and video recordings.

Analytics in sports first began with measurement of what could be easily measured – goals (of course!), strokes, hits, etc. By distilling all that goes on during the activity into a few dimensions that allow for quantification and comparison, analytics helps to explain and so increase comprehension and engagement. Increasingly complex measurements are being engineered from ever larger datasets to enhance predictions and decision-making. Both short-term outcomes and strategies that may be decided in game, and for long-term considerations such as player health are at stake.

Challenge

Challenge. In some cases, Goverlytics^® has to start creating statistics for legislative sessions from simple audio tracks. Audio is transcribed into words of a language. Then the language words (and concatenations of them) are binned into topic discourse, by means language models and topic classifications. Finally, topic classifications are used to index parts of the legislative activity that are likely to be interesting for a broader public. This process is akin to the distillation of a sporting match into a highlights reel or abbreviated match summary e.g. What topics were discussed the most? Who talked about those topics? Were there any significant new topics, or was voting and discussion about previously known topics? Were there significant outliers? Smash hits?

Because legislative sessions can go on for hours with very little information of predictive or decision-making value, it can be costly to process raw data to reach insight. The challenge is to find shorter paths to interesting bits of discourse. Can methods from signal analysis or related mathematical fields be used to more efficiently signpost insight into legislative data? Unsupervised learning techniques may provide some guidance. However, a successful solution will reveal what in the legislative activity is deserving of attention from a policy point of view, ether by connecting with a known policy ontology (such as comparative agendas codebook, or by surfacing issues that should be connected to a known ontology.

Data

At a minimum, APIs covering topic data for various legislative leagues (Canada, BC, Alberta, etc.) will be made available to the M2PI team. These APIs reliably serve data concerning legislative ‘players’ and their topic-related interventions over a number of legislative sessions. Corresponding audio will also be supplied.

Further datasets concerning elections, voting, and financial data may be made available – depending time available, which legislative leagues the M2PI team elects to study, and how they choose to analyse.

• Finance data are available from OECD, Statistics Canada, and legislative ’leagues’ themselves.
• Topics are standardized along Comparative Agendas Project (CAP) lines
• Charts of accounts for finance overlap topic categories, but do not correspond exactly.
• Voting data for bills and motions may be available for certain legislatures.
• Audio files are available for whatever legislative level is chosen for study by the M2PI team.