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Immigration policy codebook based on Benoit et al. (2016)

data_codebook_immigration.Rd

A qlm_codebook object defining instructions for annotating whether a text pertains to immigration policy and, if so, the stance toward immigration openness. This codebook replicates the crowd-sourced annotation task from Benoit et al. (2016) and is designed to work with data_corpus_manifsentsUK2010sample.

Usage

data_codebook_immigration

Format

A qlm_codebook object containing:

name

Task name: "Immigration policy coding from Benoit et al. (2016)"

instructions

Coding instructions for identifying whether sentences from UK 2010 election manifestos pertain to immigration policy, and if so, rating the policy position expressed

schema

Response schema with two fields: llm_immigration_label (Enum: "Not immigration" or "Immigration" indicating whether the sentence relates to immigration policy), and llm_immigration_position (Integer from -1 to 1, where -1 = pro-immigration, 0 = neutral, and 1 = anti-immigration)

input_type

"text"

levels

Named character vector: llm_immigration_label = "nominal", llm_immigration_position = "ordinal"

References

Benoit, K., Conway, D., Lauderdale, B.E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data. American Political Science Review, 110(2), 278–295. doi:10.1017/S0003055416000058

See also

qlm_codebook(), qlm_code(), data_corpus_manifsentsUK2010sample

Examples

# View the codebook
data_codebook_immigration
#> quallmer codebook: Immigration policy coding from Benoit et al. (2016) 
#>   Input type:   text
#>   Instructions: You are coding sentences from political texts from the 2010 ...
#>   Output schema:ellmer::TypeObject
#>   Levels:
#>     immigration_label: nominal
#>     immigration_position: ordinal

if (FALSE) { # \dontrun{
# Use with UK manifesto sentences (requires API key)
if (requireNamespace("quanteda", quietly = TRUE)) {
  coded <- qlm_code(data_corpus_manifsentsUK2010sample,
                    data_codebook_immigration,
                    model = "openai/gpt-4o-mini")

  # Compare with crowd-sourced annotations
  crowd <- as_qlm_coded(
    data.frame(
      .id = docnames(data_corpus_manifsentsUK2010sample),
      docvars(data_corpus_manifsentsUK2010sample)
    ),
    is_gold = TRUE
  )

  qlm_validate(coded, gold = crowd)

}
} # }