Bibliographic record
Abstract
In this article I explore the challenges faced by First Nations language interpreters working in Australia’s justice system in relation to the explicit requirement of impartiality/neutrality and the implicit expectation of invisibility in their day-to-day work. I interrogate the notion of (in)visibility and explore its potential to contribute to the marginalisation of First Nations interpreters in legal settings and beyond. In particular, I focus on the relationship between impartiality/neutrality and the visibility of First Nations interpreters. I argue that while impartiality is a stance that can be consciously adopted by professional interpreters, complete neutrality is an impossible and unfair requirement given how neutrality can be impacted by kinship relations, historical racial politics, community expectations, and the power differentials inherent to the justice system. The data analysed are drawn from fieldwork conducted between 2018 and 2019 in the Katherine region of Australia’s Northern Territory. The data include field notes, court observations, as well as interviews with First Nations language interpreters, legal professionals, and judicial officers.
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 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".