Keywords in Written Academic Legal Texts: A Corpus-Derived List
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents the Written Academic Legal Vocabulary (WALV), a discipline-specific genre-focused list of keywords in a corpus of academic legal texts. To generate this list, a purpose-customized corpus of full-length academic texts is created and analyzed with the help of corpus-based analytical tools. Items on the list are chosen based on criteria such as frequency of occurrence, range and keyness. The keywords recur more frequently in a specialized corpus than in a general reference corpus, a finding that attests to the pedagogical utility of these expressions as possible focus of explicit instruction. The final list consists of 298 headwords and 219 families (lemmas). Findings also indicate that the list includes words belonging to different grammatical types, the most common of which are nouns. The list also incorporates a large number of abbreviations, shortenings and acronyms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it