Corpus-based critical discourse analysis as a method of exploring underlying ideologies and self-representation strategies in legal texts
Bibliographic record
Abstract
Legal language is an integral and foundational party of our social reality, but it is underrepresented in interdisciplinary, critical linguistic analyses. This is perhaps because language is more objective and formulaic than media texts, which can be more subjective and emotive (Kjær and Palsbro, 2008). In this paper, I demonstrate how a corpus-based critical discourse analysis of legal language can expose hidden traces of the underlying ideologies of text creators, while demonstrating how identity can be performed in legal texts. Research is based on a half-million-word corpus of annual reports by the International Criminal Tribunal for the Former Yugoslavia (ICTY). Key semantic domain analysis (Rayson, 2008) is used to identify the most salient themes in the legal texts compared to reference corpora of general written English, indicating areas for closer analysis. Results show that legal language can be subjective and emotive. The semantic field of ‘crime’ is an expected key, but concordance analysis shows ideological skew in discursive construction of crimes/victims. For instance, ‘rape’/‘sexual assault’ co-occurs with female victims, whereas ‘torture’/‘outrages upon personal dignity’ co-occurs with males. Automated semantic categorization of collocates of Tribunal also indicate differing patterns in self-presentation. Early reports are dominated by discourse of progress/achievement while later reports are concerned with reputation/global perception. Critical analyses of large bodies of legal language are relatively rare, but extremely culturally relevant. Legal descriptions of crimes/perpetrators/victims are powerful and sometimes subjectively skewed. Further, self-representation of powerful legal bodies and their conceptualizations of ‘success’ and ‘failure’ in establishing/enforcing law will have lasting impacts on human rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".