Adults with Learning Disabilities and the Role of Self-Determination: Implications for Literacy Programs
Bibliographic record
Abstract
This article links the research on the status of adults with learning disabilities in the United States with the growing movement of selfdetermination for individuals with disabilities. From this knowledge base, practical implications are extracted for literacy providers on strategies for encouraging the development of self-determination in learners and for making program services more responsive to the needs of persons with learning disabilities. These implications include modifications in professional staff development, curriculum development, and mentoring and modeling, as well as increased learner involvement in planning. Résumé Cet article fait le lien entre la recherche sur les adultes éprouvant des difficultés d''pprentissage aux Etats-Unis et le mouvement en pleine expansion de l'affirmation de ces individus ayant des difficultés. Dans cette banque d'information, les intervenants en alphabétisation trouveront des applications pratiques sur les stratégies pour promouvoir l'affirmation des apprenants et pour mettre sur pied des programmes qui répondent mieux aux besoins de ces apprenants éprouvant des difficultés d'apprentissage. Ces applications incluent des modifications sur le plan du personnel professionnel, du développement du curriculum, du mentorat et du modelage de même que l'implication accrue de l'apprenant dans la planification.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".