Bibliographic record
Abstract
I completed this research study because I wanted to find out if assistive technologies in the elementary classroom are helping students with attention deficit disorder (ADD), attention deficit, hyperactivity disorder (ADHD) and anxiety issues to succeed, and what other people’s perspectives on the uses of those technologies were. An online survey was sent out using social media to reach parents, students, and teachers in order to get their perspectives on this question. I found that 45% of the people surveyed indicated that assistive technologies, in their opinion, are helping those students, while 13% said that they are not helping students. I also found that 72% of the people surveyed stated that, in their opinion, the use of these technologies is not giving students an unfair advantage, but rather leveling out the playing field, while 5% said that they believe that the use of digital technologies is giving students an unfair advantage. Based on my findings, I would say that these assistive technologies are benefiting students with ADD, ADHD, or anxiety issues, and that it does not give them an unfair advantage over other students, it just helps them to have the same chance at success as the others.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".