Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The University of Toronto’s Adaptive Technology Resource Centre (ATRC) conducted a technical audit and user study that examined the support being provided by popular online learning management systems for students with disabilities. Both studies revealed that none of the current systems was inclusive to all potential learners. What evolved out of the results of these studies was the ATutor Learning Content Management System, which was developed with the guiding philosophy of «accessibility and adaptability» for all. The development team’s initial focus was to develop a system that was accessible to all potential users regardless of the technologies they might be using to browse the Web. We were also intent on creating a system that would adapt to the learning needs or styles of individual learners, and providing a teaching environment that could be adapted to the pedagogical preferences of the instructors using it. Lastly, the team focused on making the system highly customizable, so groups using ATutor could easily modify the look and feel of the learning environment. ATutor continues to evolve and expand. Many related secondary projects are underway to extend its functionality and its community of users is rapidly growing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it