Animated skeuomorphic services for the web
Bibliographic record
Abstract
In Ontario, many individuals who speak and read languages other than \n \nEnglish or French use government services. This major research project explores how to make \nservices more accessible for populations who speak and read English as a second language. Following \nan analysis of services currently available, participatory design methods with Chinese speakers who \ndo not read \nor speak English showed how written language can be augmented or replaced with animations, sound, \nand representations of physical objects (such as automobiles, forms, drivers licenses, and license \nplates) to deliver services to audiences from diverse linguistic backgrounds. Initial prototyping \nsuggests that \n‘realistic’ or ‘visual’ representations can effectively augment or replace written language when \nthe aim is to convey something that is concrete, such as an automobile or license plate. When the \naim is to convey something that is slightly less concrete, such a car or home, outline drawings can \nbe effective. However, certain aspects of government services, such as legal disclaimers or privacy \ninformation, are more difficult to convey without written language, suggesting that \nwriting can be minimized, but not eliminated completely.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".