Bibliographic record
Abstract
The purpose of our collaborative research is to explore how teachers can help a student with ADHD become more accountable for their learning as they progress through their education career. A child with ADHD can be identified as having “a persistent pattern of inattention and/or hyperactivity–impulsivity that interferes with functioning or development” (CDC, 2022). Findings suggest that the earlier, in school, a teacher can identify the symptoms of ADHD in a student, the more successful a student is across their education career (DuPaul, 2014). With early identification teachers can work with their students with ADHD to identify strengths and weaknesses in terms of their learning and start to introduce appropriate intervention strategies. As the student progresses through their education career teachers can also work with the student to develop the necessary hard and soft skills to advocate for themselves and for their learning needs as they gain more independence in school. Knowing what accommodations they are entitled to, what works for them, and being able to communicate their needs is vital, especially at points of change and as they leave public schooling and enter post-secondary. Our recommendations will help educators as they learn to support their students with ADHD to help them be successful, not just within the classroom but across their schooling and beyond.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".