Global Pride: Diversity, Equity, and Inclusion in Interpreting
Bibliographic record
Abstract
This open-forum article highlights an interview conducted with Colin Allen, a Visiting Lecturer from the National Technical Institute for the Deaf at Rochester Institute of Technology and Abigail Gorman, an activist and graduate student at Birkbeck College, University of London, in the UK. In this interview, they highlight their experiences while coordinating International Sign interpreters for Global Pride, a virtual international global event that took place in June 2020. This was the first time that Global Pride has provided communication access to the international deaf LGBTIQA+ Community via sign interpreting services. (For the purposes of this article, LGBTQIA+ refers to lesbian, gay, bisexual, trans, intersex, queer, and asexual. The plus sign allows for the inclusion of different subsects, such as allies, polyamorous, androgynous, and pansexual.) Providing sign language access across multiple time zones for a 24-hour livestreamed event was a “first” for both Global Pride organizers and the two deaf interpreter coordinators. Their experiences offer interpreters and educators a glimpse in some of the many exciting developments of a world that has had to pivot a number of conferences and events to online platforms amid a global pandemic.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.066 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.033 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".