Bibliographic record
Abstract
There is a debate among educational professionals as to the value of assistive technology (AT) and its use with students with learning disabilities (LD). Students with learning disabilities may abandon the use of technology unless educators and service providers take the proper steps. What are some of the benefits of assistive technology for students with LD? What is technology abandonment? Why does it happen? And, more importantly, what can we do about it? There is an increase in the amount of students with learning disabilities attending postsecondary institutions. Johnson, Zascavage, & Gerber (2008) state that 98 % of universities in the US report having students with LD enrolled in their institutions. According to Hasselbring & Baush (2005), 10 percent of students in Canada have a learning disability. Currently, there is a social and financial commitment from our government education bodies to help these students to get the supports
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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 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".