Language and Disadvantage before the law
Bibliographic record
Abstract
Arguably the most difficult challenge for any legal system is to provide 'equal protection of the law' to everyone. In this chapter we have seen some of the social groups who experience disadvantage in the legal process, due in part to differences in language use. Sociolinguistic research has made some headway in the analysis of aspects of this disadvantage, which at the same time is being addressed to some extent by a number of practical initiatives in different jurisdictions. But much more remains to be done, in terms of both research and change to legal processes. Addressing language and disadvantage in the law - whether through research or law reform - requires an understanding of the complexities of multilingualism, as well as dialectal and cultural difference, and the needs of those who are not proficient in the dominant language variety. But further, it requires an understanding of the policies of disadvantage, and the rights of people whose difference from the dominant society plays a significant role in their participation in the legal process. Recent innovations in alternative legal processes which have been influenced by Indigenous people and practices in Australia, Canada and New Zealand give cause for optimism that the experiences of non-dominant social groups can have an increasingly positive impact in improving the provision of equality before the law.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".