conceptual_networks

Replication material for The Structure of Reasoning: Measuring Justification and Preferences in Text. . Sarah Shugars (working paper). Documentation for this paper is sorted into three folders:

• code/ Contains all code for cleaning, analyzing and visualizing data.

• data/ This paper contains human subject data. In order to maintain subjects' privacy, this folder only included post-processed aggregated data. Please contact me if you are interested in accessing the original datasets used.

• figs/ Contains all figures used within the paper.

Included scripts

parse.py Primary language model for inferring conceptual networks from text. Use as follows:

```
import parse
network = grammar_parse(text)
```

Takes: text, a string

Returns: network, a dictionary with keys 'nodes' and 'edges'.

netstats.py Calculates network statistics for a network. Use as follows:

```
import netstats
stats = network_stats(G)
```

Takes: G, a networkx object

Returns: stats, a dictionary of { stat_name : stat_value}

Scripts for MTurk dataset

mturk_process_raw.py

scoring.py

mturk_get_features.py

mturk_get_corr.py

mturk_visualizations.py

Scripts for YouGov dataset

yougov_process_raw.py

yougov_analysis.py