Bibliographic record
Abstract
This article reports preliminary survey and interview data from a 3-year study regarding language ideologies related to deaf interpreters (DIs). DIs are professional sign language interpreters who are deaf and who may work as part of a team with hearing sign language interpreters. Survey data provide a snapshot of current DI demographics and reflect that most DIs are Canadian-born and from a grandparent generation. This suggests that a precarious national sign language ecosystem currently exists in Canada. Data from an interview with one DI participant reveal how this participant, by virtue of his education in Canadian deaf schools and professional background, was positioned as a peer of other Canadian deaf professionals. Simultaneously, due to his immigrant background and accompanying lived experiences of language and multilayered repertoire, he was positioned in solidarity with deaf clients who were newcomers to Canada and multiply marginalised. This dual positioning and status enabled insights regarding dominant language ideologies among DIs and other deaf professionals.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".