Bridging the digital divide for people with intellectual disability
Bibliographic record
Abstract
Recent data from several studies and surveys confirm that our society has entered the digital and information age. Some authors mention that information and communication technologies (ICT) have the potential to enhance people’s power to act and promote equal citizen participation. These elements are particularly important for people living with intellectual disability (ID). However, it seems that the use of ICT is challenging for these people and that a digital divide has gradually formed between them and the connected citizen. The general objective of this theoretical article is to identify and illustrate the dimensions that must be taken into account to promote the digital participation of people with ID. The model is based on a qualitative analysis of scientific publications using a conceptual-style matrix (Miles & Huberman, 2003). The coding categories were derived from two main sources: the accessibility pyramid and the Human Development Model - Disability Creation Process. Five challenges or conditions associated with digital inclusion were identified: access to digital devices, sensorimotor, cognitive and technical requierements and the comprehension of codes and conventions. For each one, the obstacles and facilitators identified in the literature are described. These reflections and principles led us to propose a model in the shape of a gear. The proper operation of the gear system depends on the fit between individual resources and environmental support. The model is a first step to understand the digital inclusion of people with ID.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".