| Title: | Identify Text Written by Large Language Models using 'GPTZero' |
|---|---|
| Description: | An R interface to the 'GPTZero' API (<https://gptzero.me/docs>). Allows users to classify text into human and computer written with probabilities. Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences. |
| Authors: | Christopher T. Kenny [aut, cre]
|
| Maintainer: | Christopher T. Kenny <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.1 |
| Built: | 2024-08-23 04:16:41 UTC |
| Source: | https://github.com/christopherkenny/gptzeror |
Predict if a file was generated by AI
gptzero_predict_file(file)gptzero_predict_file(file)
file |
file to upload |
a tibble of sentence-level assessments
# Requires API Key abstr <- 'Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternative simulated plans that serve as a non-partisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the U.S. House less responsive to shifts in the national vote.' abstr <- gsub('\\n', ' ', abstr) # remove the "\n"s fake_abstr <- 'The 2020 congressional redistricting process in the United States has been marked by widespread partisan gerrymandering, which has led to concerns about its impact on electoral outcomes and democratic representation. This paper examines the effects of partisan gerrymandering on electoral competition in the 2020 congressional elections. Using data from previous election cycles and simulations of the 2020 elections, we find that while partisan gerrymandering mostly cancels out at the national level, it reduces competition in individual districts, making them less competitive and reducing voter choice. We also find that the impact of gerrymandering on electoral outcomes varies significantly by state and political party, suggesting that redistricting can have significant implications for democratic representation and political power in the United States. The findings of this study have important implications for policymakers, as well as advocates of fair and democratic elections.' fake_abstr <- gsub('\\n', ' ', fake_abstr) # remove the "\n"s tf_abstr <- tempfile(fileext = '.txt') cat(abstr, file = tf_abstr) tf_fake <- tempfile(fileext = '.txt') cat(fake_abstr, file = tf_fake) gptzero_predict_file(tf_abstr) gptzero_predict_file(tf_fake)# Requires API Key abstr <- 'Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternative simulated plans that serve as a non-partisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the U.S. House less responsive to shifts in the national vote.' abstr <- gsub('\\n', ' ', abstr) # remove the "\n"s fake_abstr <- 'The 2020 congressional redistricting process in the United States has been marked by widespread partisan gerrymandering, which has led to concerns about its impact on electoral outcomes and democratic representation. This paper examines the effects of partisan gerrymandering on electoral competition in the 2020 congressional elections. Using data from previous election cycles and simulations of the 2020 elections, we find that while partisan gerrymandering mostly cancels out at the national level, it reduces competition in individual districts, making them less competitive and reducing voter choice. We also find that the impact of gerrymandering on electoral outcomes varies significantly by state and political party, suggesting that redistricting can have significant implications for democratic representation and political power in the United States. The findings of this study have important implications for policymakers, as well as advocates of fair and democratic elections.' fake_abstr <- gsub('\\n', ' ', fake_abstr) # remove the "\n"s tf_abstr <- tempfile(fileext = '.txt') cat(abstr, file = tf_abstr) tf_fake <- tempfile(fileext = '.txt') cat(fake_abstr, file = tf_fake) gptzero_predict_file(tf_abstr) gptzero_predict_file(tf_fake)
Predict if text was generated by AI
gptzero_predict_text(text)gptzero_predict_text(text)
text |
text to upload |
a tibble of sentence-level assessments
# Requires API Key abstr <- 'Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternative simulated plans that serve as a non-partisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the U.S. House less responsive to shifts in the national vote.' abstr <- gsub('\\n', ' ', abstr) # remove the "\n"s gptzero_predict_text(abstr)# Requires API Key abstr <- 'Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternative simulated plans that serve as a non-partisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the U.S. House less responsive to shifts in the national vote.' abstr <- gsub('\\n', ' ', abstr) # remove the "\n"s gptzero_predict_text(abstr)
Add GPTZero API key to .Renviron.
gptzero_set_key(key, overwrite = FALSE, install = FALSE)gptzero_set_key(key, overwrite = FALSE, install = FALSE)
key |
Character. API key to add to add. |
overwrite |
Defaults to FALSE. Boolean. Should existing |
install |
Defaults to FALSE. Boolean. Should this be added '~/.Renviron' file? |
key, invisibly
## Not run: gptzero_set_key('1234') ## End(Not run)## Not run: gptzero_set_key('1234') ## End(Not run)
Check or Get GPTZero API Key
gptzero_has_key() gptzero_get_key()gptzero_has_key() gptzero_get_key()
logical if has, key invisibly if get
gptzero_has_key()gptzero_has_key()