Leveraging inclusion and diversity as Canada's digital advantage
Bibliographic record
Abstract
Led by Jutta Treviranus, it is an internationally recognized centre of expertise in the inclusive design of emerging information and communication systems and practices.The centre conducts proactive research and development to ensure that emerging information technologies accommodate the full range of human diversity including culture, language, age, and ability.Through a large number of multi-partner, multi-sector projects, the centre has formed an extensive international network.Experts at the centre have helped to create policy, standards and exemplars in a broad range of domains including education, banking, public Web sites, edemocracy, and cultural exchange.The Adaptech Research Network (http://adaptech.dawsoncollege.qc.ca/) consists of a team of academics, students and consumers.It conducts research on facilitators and barriers to academic success as well as on the use of computer, information, and adaptive technologies by Canadian college and university students with diverse disabilities.Based at Dawson College and affiliated with McGill University, it conducts research bilingually.The goal of the Network is to provide empirically based information to assist in decision making that ensures that new policies, software and hardware reflect the needs and concerns of a variety of individuals.The Adaptech Research Network is co-directed by
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".