Pursuing Universal Accessibility for Everyone: The Linguistic Experience at Partition/Ensemble Conference
Bibliographic record
Abstract
Universal accessibility (or design) is a trend that promotes accessibility for everyone in various ways. One of its attributes is to ensure that everyone has equal learning opportunities, especially with the ‘access to information’ format. This applies to arranging a conference that includes conference organizers, plenary speakers, performers, conference presenters, and audio describers preparing to provide information and sensorial accessibility to the conference participants. Unfortunately for contemporary conferences, individuals with different needs are likely to experience language barriers due to their linguistic differences, hearing loss, and/or challenges in understanding and/or accessing visual information. A performing arts conference, Partition/Ensemble 2020, hosted by the Canadian Association of Theatre Research, serves as a case study for examining the process of arranging and providing language interpreters and text transcriptions, including audiovisual descriptions. During the COVID pandemic in the summer of 2020, the conference organizers decided to have a relaxed virtual conference. This designation had an impact on the preparation with four languages in different modalities: English (spoken and written), French (spoken and written), American Sign Language (signed), and Langue des signes québécoise (signed). From this linguistic learning experience, individuals who participated in this conference (e.g. conference organizers, plenary speakers, and audio describers) share their thoughts and insights for the implementation of an accessible conference (whether hosted in-person or online) with the goal of reducing language barriers. The authors of this article consider what it means to incorporate a diversity of languages simultaneously with different modalities and the challenges of accessibility with this endeavour.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.015 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".